Publications

Molecular modeling of thermally activated chemistry in condensed phases is essential to understand polymerization, depolymerization, and other processing steps of molecular materials. Current methods typically combine molecular dynamics (MD) simulations to describe short-time relaxation with stochastic descriptions of predetermined chemical reactions. Possible reactions are often selected on the basis of geometric criteria, such as the capture distance between reactive atoms. Although these simulations have provided valuable insight, the approximations used to determine possible reactions often lead to significant molecular strain and unrealistic structures. We show that the local molecular environment surrounding the reactive site plays a crucial role in determining the resulting molecular strain energy and, in turn, the associated reaction rates. We develop a graph neural network capable of predicting the strain energy associated with a cyclization reaction from the prereaction, local, molecular environment surrounding the reactive site. The model is trained on a large data set of condensed-phase reactions during the activation of polyacrylonitrile (PAN) obtained from MD simulations and can be used to adjust relative reaction rates in condensed systems and advance our understanding of thermally activated chemical processes in complex materials.

Nanoscale precipitates have been proved to affect the transformation behavior of shape memory alloys (SMA) significantly. In this work, the effects of Heusler precipitates (Ni2TiAl) on the Ni50Ti24.5Hf22.5Al3 SMA were systematically investigated. Density functional theory (DFT) calculations showed that coherent Heusler precipitates can mechanically stabilize the austenite phase, indicating the decrease of transformation temperatures. This trend was further confirmed experimentally. The depression of transformation temperatures with the increase of heat treatment time was observed as nanoscale coherent precipitates nucleated and grew. This was followed by an abrupt increase in transformation temperatures when aging time exceeded 200 hours at 700 °C. We attribute this increase to the loss of coherency between the Heusler precipitates and matrix which weakened their mechanical coupling. Detailed microstructure characterization by high-resolution transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS) were provided to support the finding.

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe2. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress-strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation — phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. This work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

Machine learning has become a central technique for modeling in science and engineering, either complementing or as surrogates to physics-based models. Significant efforts have recently been devoted to models capable of predicting field quantities, but the limitations of current state-of-the-art models in describing complex physics are not well understood. We characterize the ability of generative diffusion models and generative adversarial networks (GANs) to describe the Ising model. We find diffusion models trained using equilibrium configurations obtained using Metropolis Monte Carlo for a range of temperatures around the critical temperature that can capture average thermodynamic variables across the phase transformation and extrapolate to higher and lower temperatures. The model also captures the overall trends of physical properties associated with fluctuations (specific heat and susceptibility) except at the non-ergodic low temperatures and non-trivial scale-free correlations at the critical temperature, albeit with some difference in the critical exponent compared to Monte Carlo simulations. GANs perform more poorly on thermodynamic properties and are susceptible to mode collapse without careful training. This investigation highlights the potential and limitations of generative models in capturing the complex phenomena associated with certain physical systems.

Localizing the energetic output from detonation waves has been a long-standing challenge in applied detonation physics. Here, energy localization is achieved via machined millimeter scale voids in pressed samples of PBX 9501, an HMX (1,3,5,7-Tetranitro-1,3,5,7-tetrazocane)-based plastic bonded explosive. A main mechanism of energy localization in these systems, the formation of hydrodynamic jets of dense product gases, is characterized experimentally using a semicylindrical geometry in witness plate impact experiments and streak imaging of the jet propagating into the air. The distance at which the jet is optimally developed is identified and the supersonic flow structure in the vicinity of this feature is explored using hydrocode simulations. This analysis found that most of the kinetic energy of the hydrodynamic jet arises from pressure gradients induced by geometrically mediated squeeze flow lateral to the direction of detonation propagation. This work presents a new development in the control of energetic output from detonation waves and applications to detonation wave shaping are discussed.

Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data is sparse, severely limiting their accuracy and applicability. To improve predictions, techniques such as transfer learning and multitask learning have been used. The performance of multitask learning models depends on the strength of the underlying correlations between tasks and the completeness of the data set. Standard multitask models tend to underperform when trained on sparse data sets with weakly correlated properties. To address this gap, we fuse deep-learned embeddings generated by independent pretrained single-task models, resulting in a multitask model that inherits rich, property-specific representations. By reusing (rather than retraining) these embeddings, the resulting fused model outperforms standard multitask models and can be extended with fewer trainable parameters. We demonstrate this technique on a widely used benchmark data set of quantum chemistry data for small molecules as well as a newly compiled sparse data set of experimental data collected from literature and our own quantum chemistry and thermochemical calculations.

BaZrS3 is a chalcogenide perovskite that has shown promise as a photovoltaic absorber, but its performance is limited because of defects and impurities, which have a direct influence on carrier concentrations. Functional dopants that show lower donor-type or acceptor-type formation energies than naturally occurring defects can help tune the optoelectronic properties of BaZrS3. In this work, we applied first-principles computations to comprehensively investigate the defect landscape of BaZrS3, including all intrinsic defects and a set of selected impurities and dopants. BaZrS3 intrinsically exhibits n-type equilibrium conductivity under both S-poor and S-rich conditions, which remains largely unchanged in the presence of O and H impurities. La and Nb dopants created stable donor-type defects which make BaZrS3 even more n-type, whereas As and P dopants formed amphoteric defects with relatively high formation energies. This work highlights the difficulty of creating p-type BaZrS3 owing to the low formation energies of donor defects, both intrinsic and extrinsic. Defect formation energies were also used to compute expected defect concentrations and make comparisons with experimentally reported values. Our dataset of defects in BaZrS3 paves the path for training machine learning models to subsequently perform larger-scale prediction and screening of defects and dopants across many chalcogenide perovskites, including cation-site or anion-site alloys.

Density functional theory (DFT) is widely used to study the behavior of materials at high pressures, complementing challenging and often costly experiments. While the accuracy of DFT and the effect of various approximations and corrections have been extensively studied for materials properties around ambient conditions, few studies quantified accuracy at high pressures. We focus on the accuracy of predicted equations of state (EOS) of selected materials up to the hundred GPa regime and the description of pressure-induced phase transformations. We characterize the effect of exchange-correlation functionals, pseudopotentials, dispersion and Hubbard U correction and find that lessons-learned at ambient conditions do not always translate into the high-pressure regime. We find that the Perdew-Burke-Erzerhof solid version of the generalized gradient approximation (GGA) yields the best performance in both EOS and transformation pressure compared to Perdew-Burke-Erzerhof version of GGA, local density approximations (LDA), and the Heyd-Scuseria-Ernzerhof (HSE) hybrid functional. Adding dispersion corrections known as D2 and D3 does not improve the results. Interestingly, the local density approximation performed remarkably well. We also find that the Hubbard-U correction has a significant effect on transformation pressures in strongly correlated materials systems, indicating that the U parameter must be chosen carefully. An important by-product of this study is a FAIR repository of high-pressure simulations database on nanoHUB.

Pressure-induced phase transformations in materials are of interest in a range of fields, including geophysics, planetary sciences, and shock physics. In addition, the high-pressure phases can exhibit desirable properties, eliciting interest in materials science. Despite its importance, the process of finding new high-pressure phases, either experimentally or computationally, is time-consuming and often driven by intuition. In this study, we use graph neural networks trained on density functional theory (DFT) equation of state data of 2258 materials and 7255 phases to identify potential phase transitions. The model is used to explore possible phase transitions in 7677 pairs of phases and promising cases are confirmed or denied via DFT calculations. Importantly, the new data is added to the training set, the model is refined, and a new cycle of discovery is started. Within 13 iterations, we discovered 28 new high-pressure stable phases (never synthesized through high-pressure routes nor reported in high-pressure computational works) and rediscovered 18 pressure-induced phase transitions. The results provide new insight and classification of pressure-induced phase transitions in terms of the ambient properties of the phases involved.

Many high explosive (HE) formulations are composite materials whose microstructure is understood to impact functional characteristics. Interfaces are known to mediate the formation of hot spots that control their safety and initiation. To study such processes at molecular scales, we developed all-atom force fields (FFs) for Octol, a prototypical HE formulation comprised of TNT (2,4,6-trinitrotoluene) and HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine). We extended a FF for TNT and recasted it in a form that can be readily combined with a well-established FF for HMX. The resulting FF was extensively validated against experimental results and density functional theory calculations. We applied the new combined TNT-HMX FF to predict and rank surface and interface energies, which indicate that there is an energetic driver for coarsening of microstructural grains in TNT-HMX composites. Finally, we assess the impact of several microstructural environments on the dynamic melting of TNT crystal under ultrafast thermal loading. We find that both free surfaces and planar material interfaces are effective nucleation points for TNT melting. However, MD simulations show that TNT crystal is prone to superheating by at least 50 K on subnanosecond time scales and that the degree of superheating is inversely correlated with surface and interface energy. The modeling framework presented here will enable future studies on hot spot formation processes in accident scenarios that are governed by strong coupling between microstructural interfaces, material mechanics, momentum and energy transport, phase transitions, and chemistry.

High-strength alloys are intimately connected to human development, from the bronze age to the current applications in aerospace and energy. State-of-the-art alloys are engineered to harness strengthening mechanisms across scales, from crystal-level processes to complex hierarchical microstructures that are designed to hinder the mobility of dislocations and other carriers of plasticity. In this context, complex concentrated alloys (CCAs) are attractive since they can exhibit very high strength at the single-phase level, which can be further enhanced via incorporation of the second phase and microstructural optimization. Unfortunately, the optimization of CCAs is notoriously difficult due to the high dimensionality of the design space. We demonstrate that a combination of physics-based modeling, machine learning, experimental fabrication, and multi-resolution characterization results in the discovery of the hardest Al-containing BCC-based alloy, surpassing the current state of the art by 31%. Importantly, this is accomplished with only 24 experiments within a design space consisting of 67 536 possible candidates. The approach can be generalized to other alloys, and the resulting materials are of interest in applications ranging from aerospace to nuclear power.

Large-scale nonequilibrium molecular dynamics simulations of RDX shocked along [100] reveal a steady-state regime of elastic-plastic shock wave propagation along slip-hindered directions in molecular crystals. For a shock with a particle velocity of 1 km/s, steady state is achieved after approximately 100 ps or 0.5 microns from the impact plane. The transient regime is characterized by a dynamic evolution of plasticity, which transitions from the simultaneous nucleation of a high density of shear bands to the growth of fewer shear bands, each resulting in significant localized shear strain and temperature. This evolution of the plastic wave leads to a significant attenuation of the elastic precursor, with a 20% drop in stress along the shock direction. This significant variation in the structure of the shock wave over distances larger than most atomistic simulations to date highlights the need for continuing efforts toward scalable molecular simulations and the challenges in multiscale modeling of dynamical properties.

Active learning (AL) is a powerful sequential optimization approach that has shown great promise in the discovery of new materials. However, a major challenge remains the acquisition of the initial data and the development of workflows to generate new data at each iteration. In this study, we demonstrate a significant speedup in an optimization task by reusing a published simulation workflow available for online simulations and its associated data repository, where the results of each workflow run are automatically stored. Both the workflow and its data follow FAIR (findable, accessible, interoperable, and reusable) principles using nanoHUB's infrastructure. The workflow employs molecular dynamics to calculate the melting temperature of multi-principal component alloys. We leveraged all prior data not only to develop an accurate machine learning model to start the sequential optimization but also to optimize the simulation parameters and accelerate convergence. Prior work showed that finding the alloy composition with the highest melting temperature required testing several alloy compositions, and establishing the melting temperature for each composition took, on average, multiple simulations. By developing a workflow that utilizes the FAIR data in the nanoHUB database, we reduced the number of simulations per composition to one and found the alloy with the lowest melting temperature testing only three compositions. This second optimization, therefore, shows a speedup of 10x as compared to models that do not access the FAIR databases.

Among emerging energy materials, halide and chalcogenide perovskites have garnered significant attention over the last decade owing to the abundance of their constituent species, low manufacturing costs, and their highly tunable composition-structure-property space. Navigating the vast perovskite compositional landscape is possible using density functional theory (DFT) computations, but they are not easily extended to predictions of the synthesizability of new materials and their properties. As a result, only a limited number of compositions identified to have desirable optoelectronic properties from these calculations have been realized experimentally. One way to bridge this gap is by learning from the experimental literature about how the perovskite composition-structure space relates to their likelihood of laboratory synthesis. Here, we present our efforts in combining high-throughput DFT data with experimental labels collected from the literature to train classifier models employing various materials descriptors to forecast the synthesizability of any given perovskite compound. Our framework utilizes the positive and unlabeled learning strategy and makes probabilistic estimates of the synthesis likelihood based on DFT-computed energies and the prior existence of similar synthesized compounds. Our data and models can be readily accessed via a findable, accessible, interoperable, and reproducible nanoHUB tool.

The shock initiation of energetic materials is mediated by the localization of mechanical energy into hotspots. These originate through the interaction of the shock and material microstructure; the most potent hotspots are formed by the collapse of porosity. Recent work using molecular dynamics (MD) has shed light on the molecular mechanisms responsible for the shock-to-deflagration transition following pore collapse in pure energetic materials. However, explosive formulations are composites of energetic crystals and a polymer binder, which differs from the prior focus on pure materials. The role of polymer phases in hotspot formation and its criticality remains poorly understood. We use reactive MD simulations to investigate the role of polystyrene and polyvinyl nitrate films around pores in the shock-induced pore collapse of RDX. The polymer affects the hotspots' temperature and their criticality. While the presence of inert polymer often delays or hinders chemical reactions of the energetic material, certain geometries accelerate chemistry. The simulations provide a mechanistic understanding of these phenomena.

Data science and artificial intelligence are playing an increasingly important role in the physical sciences. Unfortunately, in the field of energetic materials data scarcity limits the accuracy and even applicability of ML tools. To address data limitations, we compiled multi-modal data: both experimental and computational results for several properties. We find that multi-task neural networks can learn from multi-modal data and outperform single-task models trained for specific properties. As expected, the improvement is more significant for data-scarce properties. These models are trained using descriptors built from simple molecular information and can be readily applied for large-scale materials screening to explore multiple properties simultaneously. This approach is widely applicable to fields outside energetic materials.

Condensed phase molecular systems organize in a wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.

Little of the data — arguably the most important product of worldwide materials research — are shared in forms usable by others. The small and biased proportion of results published are buried in plots and text licensed by journals. This situation wastes resources, hinders innovation, and, in the current era of data-driven discovery, is no longer tenable. In this article, we propose specific synergistic, collaborative, and global actions to enable the assembly of large quantities of Findable, Accessible, Interoperable, Reusable (FAIR) materials data. We provide a context to comprehend what FAIR data can mean for materials scientists, a motivation for the adoption of FAIR principles, and a perspective on how widespread adoption of FAIR data can advance their science.

The chemical process of stabilization is a critical step in the fabrication of polyacrylonitrile (PAN)-based carbon fibers; it transforms the spun polymer precursor into a thermally stable ladder compound capable of undergoing the processes of carbonization and graphitization. The molecular structure of the stabilized polymer strongly influences the microstructure and, consequently, the properties of the resulting fiber. However, molecular models of the process of stabilization are lacking, and so is an understanding of the role of the molecular structure of the spun fiber. We developed a model that combines stochastic chemical reactions with molecular dynamics (MD) to simulate the process of stabilization of PAN in atomic detail; we describe dehydrogenation, activation, and cyclization. The rates of the various reactions are adjustable parameters and depend on the local environment of the active sites. We compared the stabilization of unstretched amorphous PAN and a sample that had undergone stretching and, thus, includes molecularly ordered and disordered regions. We find that the molecular alignment accomplished via spinning does not increase the amount of cyclization but favors intramolecular over intermolecular reactions and the number of contiguous rings.

The area-normalized change of mass (Δm/A) with time during the oxidation of metallic alloys is commonly used to assess oxidation resistance. Analyses of such data can also aid in evaluating underlying oxidation mechanisms. We performed an exhaustive literature search and digitized normalized mass change vs. time data for 407 alloys. To maximize the impact of these and future mass uptake data, we developed and published an open, online, computational workflow that fits the data to various models of oxidation kinetics, uses Bayesian statistics for model selection, and makes the raw data and model parameters available via a queryable database. The tool, Refractory Oxidation Database (RefOxDB), uses nanoHUB's Sim2Ls to make the workflow and data (including metadata) findable, accessible, interoperable, and reusable (FAIR). We find that the models selected by the original authors do not match the most likely one according to the Bayesian information criterion (BIC) in 71% of the cases. Further, in 56% of the cases, the published model was not even in the top 3 models according to the BIC. The RefOxDB tool is open access and researchers can add their own raw data for analysis and to share their work with the community. Such consistent and systematic analysis of open, community-generated data can significantly accelerate the development of machine-learning models for oxidation behavior and assist in the understanding and improvement of oxidation resistance.

Microstructure and defects strongly affect martensitic transformations in metallic alloys. Significant progress has been made in understanding the atomic-level processes that control the role of grain boundaries and precipitates in these solid-to-solid phase transformations. Yet, the role of dislocations and their structures on martensitic transformation temperature and the resulting microstructure remains unclear. Therefore, we used large-scale molecular dynamics simulations to study the forward and reverse transformation of a martensitic material modeled after Ni63Al37 under cyclic thermal loading. The simulations reveal that dislocations in the austenite phase act as one-dimensional seeds for the martensite phase, which is present at temperatures significantly above the martensite start value. We find a reduction in the dislocation density during cyclic thermal loading, which results in the increase in martensite and austenite transition temperatures, in agreement with experiments. Importantly, we extracted a critical martensitic nuclei size for developing stable domains and found that relatively low dislocation densities are needed to grow independent martensitic variants resulting in a multi-domain structure.

The crystallization of complex, concentrated alloys can result in atomic-level short-range order, composition gradients, and phase separation. These features govern the properties of the resulting alloy. While nucleation and growth in single-element metals are well understood, several open questions remain regarding the crystallization of multi-principal component alloys. We use molecular dynamics to model the crystallization of a five-element, equiatomic alloy modeled after CoCrCuFeNi upon cooling from the melt. Stochastic, homogeneous nucleation results in nuclei with a biased composition distribution, rich in Fe and Co. This deviation from the random sampling of the overall composition is driven by the internal energy and affects nuclei of a wide range of sizes, from tens of atoms all the way to super-critical sizes. This results in short-range order and compositional gradients at nanometer scales.

Ge-Sb-Te (GST) alloys are leading phase-change materials for data storage due to the fast phase transition between amorphous and crystalline states. Ongoing research aims at improving the stability of the amorphous phase to improve retention. This can be accomplished by the introduction of carbon as a dopant to Ge2Sb2Te5, which is known to alter the short- and mid-range structure of the amorphous phase and form covalently bonded C clusters, both of which hinder crystallization. The relative importance of these processes as a function of C concentration is not known. We used molecular dynamics simulation based on density functional theory to study how carbon doping affects the atomic structure of GST-C. Carbon doping results in an increase in tetrahedral coordination, especially of Ge atoms, and this is known to stabilize the amorphous phase. We observe an unexpected, non-monotonous trend in the number of tetrahedral bonded Ge with the amount of carbon doping. Our simulations show an increase in the number of tetrahedral bonded Ge up to 5 at.% C, after which the number saturates and begins to decrease above 14 at.% C. The carbon atoms aggregate into clusters, mostly in the form of chains and graphene flakes, leaving less carbon to disrupt the GST matrix at higher carbon concentrations. Different degrees of carbon clustering can explain divergent experimental results for recrystallization temperature for carbon doped GST.

Predictive models for the performance of explosives and propellants are important for their design, optimization, and safety. Thermochemical codes can predict some of these properties from fundamental quantities such as density and formation energies that can be obtained from first principles. Models that are simpler to evaluate are desirable for efficient, rapid screening of material screening. In addition, interpretable models can provide insight into the physics and chemistry of these materials that could be useful to direct new synthesis. Current state-of-the-art performance models are based on either the parametrization of physics-based expressions or data-driven approaches with minimal interpretability. We use parsimonious neural networks (PNNs) to discover interpretable models for the specific impulse of propellants and detonation velocity and pressure for explosives using data collected from the open literature. A combination of evolutionary optimization with custom neural networks explores and trains models with objective functions that balance accuracy and complexity. For all three properties of interest, we find interpretable models that are Pareto optimal in the accuracy and simplicity space.

Numerous crystal- and microstructural-level mechanisms are at play in the formation of hotspots, which are known to govern high explosives initiation behavior. Most of these mechanisms, including pore collapse, interfacial friction, and shear banding, involve both compressive and shear work done within the material and have thus far remained difficult to separate. We assess hotspots formed at shocked crystal-crystal interfaces using quasi-1D molecular dynamics simulations that isolate effects due to compression and shear. Two high explosive materials are considered (TATB and PETN) that exhibit distinctly different levels of molecular conformational flexibility and crystal packing anisotropy. Temperature and intramolecular strain energy localization in the hotspot are assessed through parametric variation of the crystal orientation and two velocity components that respectively modulate compression and shear work. The resulting hotspots are found to be highly localized to a region within 5-20 nm of the crystal-crystal interface. Compressive work plays a considerably larger role in localizing temperature and intramolecular strain energy for both materials and all crystal orientations considered. Shear induces a moderate increase in energy localization relative to unsheared cases only for relatively weak compressive shock pressures of approximately 10 GPa. These results help isolate and rank the relative importance of hotspot generation mechanisms and are anticipated to guide the treatment of crystal-crystal interfaces in coarse-grained models of polycrystalline high-explosive materials.

Mechanophores (MPs), stimuli-responsive molecules that respond chromatically to mechanochemical reactions, are important for understanding the coupling between mechanics and chemistry as well as in engineering applications. However, the atomic-level understanding of their activation originates from gas phase studies or under simple linear elongation forces directly on molecules or polymer chains containing MPs. The effect of many-body distortions, pervasive in condensed-phase applications, is not understood. Therefore, we performed large-scale molecular dynamics (MD) simulations of a poly(methyl methacrylate)-spiropyran copolymer under dynamic mechanical loading and studied the activation of the MP under various conditions from dynamical compression to tension during unloading. Detailed analysis of the all-atom MD trajectories shows that the MP blocks experience significant many-body intramolecular distortion that can significantly decrease the activation barrier as compared with when deformation rates are slow relative to molecular relaxation time scales. We find that the reactivity of MPs under material compression states is governed by many-body effects of intramolecular torsions, whereas under tension, the reactions are governed by tensile stresses.

Molecular strains can greatly alter chemical reactions in covalent systems. Experimental and computational tools designed to characterize this have focused on simple elongation forces. Yet, mechanical loading in condensed matter results in complex, many-body deformations. Hence, we use four-body external potentials designed to reproduce these strains with reactive molecular dynamics. Mimicking these deformations results in significant lowering of activation barriers and different reaction pathways in the energetic material 1,3,5-trinitro-2,4,6-triaminobenzene (TATB) and a lower-energy reaction pathway for isomerization in spiropyran.

When high-energy-density materials are subjected to thermal or mechanical insults at extreme conditions (shock loading), a coupled response between the thermo-mechanical and chemical behaviors is systematically induced. We develop a reaction model for the fast chemistry of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) at the mesoscopic scale, where the chemical behavior is determined by underlying microscopic reactive simulations. The slow carbon cluster formation is not discussed in the present work. All-atom reactive molecular dynamics (MD) simulations are performed with the ReaxFF potential, and a reduced-order chemical kinetics model for TATB is fitted to isothermal and adiabatic simulations of single crystal chemical decomposition. Unsupervised machine learning techniques based on non-negative matrix factorization are applied to MD trajectories to model the decomposition kinetics of TATB in terms of a four-component model. The associated heats of reaction are fit to the temperature evolution from adiabatic decomposition trajectories. Using a chemical species analysis, we show that non-negative matrix factorization captures the main chemical decomposition steps of TATB and provides an accurate estimation of their evolution with temperature. The final analytical formulation, coupled to a diffusion term, is incorporated into a continuum formalism, and simulation results are compared one-to-one against MD simulations of 1D reaction propagation along different crystallographic directions and with different initial temperatures. A good agreement is found for both the temporal and spatial evolution of the temperature field.

The need for novel materials for energy storage and generation calls for chemical control at the atomic scale in nanomaterials. Ordered double-transition-metal MXenes expanded the chemical diversity of the family of atomically layered 2D materials since their discovery in 2015. However, atomistic tunability of ordered MXenes to achieve ideal composition-property relationships has not been yet possible. In this study, we demonstrate the synthesis of Mo2+αNb2−αAlC3 MAX phases (0 ≤ α ≤ 0.3) and confirm the preferential ordering behavior of Mo and Nb in the outer and inner M layers, respectively, using density functional theory, Rietveld refinement, and electron microscopy methods. We also synthesize their 2D derivative Mo2+αNb2−αC3Tx MXenes and exemplify the effect of preferential ordering on their hydrogen evolution reaction electrocatalytic behavior. This study seeks to inspire further exploration of the ordered double-transition-metal MXene family and contribute composition-behavior tools toward application-driven design of 2D materials.

Doped lithium lanthanum zirconium oxide (LLZO) is a promising class of solid electrolytes for lithium-ion batteries due to their good electrochemical stability and compatibility with Li metal anodes. Ionic diffusivity in these ceramics is known to occur via correlated, vacancy-mediated jumps of Li+ between alternating tetrahedral and octahedral sites. Aliovalent doping at the Zr site increases the concentration of vacancies in the Li+ sublattice and cation diffusivity, but such an increase is universally followed by a decrease for Li+ concentration lower than 6.3-6.5 Li molar content. Molecular dynamics simulations based on density functional theory show that the maximum in diffusivity originates from competing effects between the increased vacancy concentration and the increasing occupancy of the low-energy tetrahedral sites by Li+, which increases the overall activation energy associated with diffusion. For the relatively high temperatures of our simulations, Li+ concentration plays a dominant role in transport as compared to dopant chemistry.

MXenes are an emerging class of 2D materials of interest in applications ranging from energy storage to electromagnetic shielding. MXenes are synthesized by selective etching of layered bulk MAX phases into sheets of 2D MXenes. Their chemical tunability has been significantly expanded with the successful synthesis of double transition metal MXenes. While knowledge of the structure and energetics of double transition metal MAX phases is critical to designing and optimizing new MXenes, only a small subset of these materials have been explored. We present a comprehensive dataset of key properties of MAX phases obtained using density functional theory within the generalized gradient approximation exchange-correlation functionals. Energetics and structure of 8,712 MAX phases have been calculated and stored in a queryable, open database hosted at nanoHUB.

The combination of optical and mechanical properties of calcium lanthanum sulfide (CLS) makes it an attractive material for optical applications. CLS is a γ-phase La2S3/CaS solid solution above 50:50 La2S3/CaS. Experiments have characterized the effect of composition on the structural, thermal, and optical properties. However, the effect of impurities and other defects, such as porosity, on resulting properties is difficult to deconvolute. We used the density functional theory (DFT) simulations to characterize the effect of La2S3/CaS relative fractions on structural, electronic, mechanical, and vibrational properties of single crystalline CLS solid solutions. We find a decrease in the stiffness with CaS fraction and confirm the experimentally observed increase in the lattice parameter with La2S3 fraction. We also characterized the point defects, including sulfur vacancies, oxygen substituting sulfur, oxygen, and sulfur impurities on lanthanum sublattice vacant sites, and sulfur substituting calcium (vS, OS, OLa, and SLa, respectively). Our defect studies in 90:10 CLS find the neutral charge state configuration to be energetically favorable for all tested configurations except for the sulfur vacancies (vS).

The response of materials to shock loading is important to planetary science, aerospace engineering, and energetic materials. Thermally activated processes, including chemical reactions and phase transitions, are significantly accelerated by energy localization into hotspots. These result from the interaction of the shockwave with the materials’ microstructure and are governed by complex, coupled processes, including the collapse of porosity, interfacial friction, and localized plastic deformation. These mechanisms are not fully understood and the lack of models limits our ability to predict shock to detonation transition from chemistry and microstructure alone. We demonstrate that deep learning can be used to predict the resulting shock-induced temperature fields in composite materials obtained from large-scale molecular dynamics simulations with the initial microstructure as the only input. The accuracy of the Microstructure-Informed Shock-induced Temperature net (MISTnet) model is higher than the current state of the art and its evaluation requires a fraction of the computation cost.

Shock-induced plasticity and structural changes in energetic molecular crystals are well documented. These processes couple with the leading shock wave and affect its propagation, resulting in long, transient responses that are challenging to capture with all-atom simulations due to their time scale. Hence, the effects of this coupling and the transient shock response on the formation of hotspots and the initiation of chemistry remain unclear. To address these challenges, we investigate the role of shock-induced plastic deformation on shock initiation with a recently developed particle-based, coarse-grain model for 1,3,5-trinitro-1,3,5-triazinane (RDX) that utilizes the generalized dissipative particle dynamics with reactions framework. This model enables reactive simulations at micron length scales, which are required to achieve steady-state shock propagation. The simulations show that the shock Hugoniot response of RDX can involve transient behavior for up to 150 ps before steady-state behavior is achieved for shock strengths above the elastic limit. Pore collapse simulations demonstrate that the intensity of the resulting hotspot will weaken as the shock transitions from transient to steady-state behavior, ultimately affecting the shock-to-deflagration transition. Our results highlight the importance of considering the mesoscopic effects of shock-induced plastic deformation in simulations of shock-to-deflagration transitions of high explosives.

Reactive force fields for molecular dynamics have enabled a wide range of studies in numerous material classes. These force fields are computationally inexpensive compared with electronic structure calculations and allow for simulations of millions of atoms. However, the accuracy of traditional force fields is limited by their functional forms, preventing continual refinement and improvement. Therefore, we develop a neural network-based reactive interatomic potential for the prediction of the mechanical, thermal, and chemical responses of energetic materials at extreme conditions. The training set is expanded in an automatic iterative approach and consists of various CHNO materials and their reactions under ambient and shock-loading conditions. This new potential shows improved accuracy over the current state-of-the-art force fields for a wide range of properties such as detonation performance, decomposition product formation, and vibrational spectra under ambient and shock-loading conditions.

Predictive models for the thermal, chemical, and mechanical response of high explosives at extreme conditions are important for investigating their performance and safety. We introduce a particle-based, reactive model of 1,3,5-trinitro-1,3,5-triazinane (RDX) with molecular resolution utilizing generalized energy-conserving dissipative particle dynamics with reactions. The model is parameterized with respect to the data from atomistic molecular dynamics simulations as well as from quantum mechanical calculations, thus bridging atomic processes to the mesoscales, including microstructures and defects. It accurately captures the response of RDX under a range of thermal loading conditions compared to atomistic simulations. In addition, the Hugoniot response of the CG model in the overdriven regime reasonably matches atomistic simulations and experiments. Exploiting the model's high computational efficiency, we investigate mesoscale systems involving millions of molecules and characterize size-dependent criticality of hotspots in RDX. The combination of accuracy and computational efficiency of our reactive model provides a tool for investigation of mesoscale phenomena, such as the role of microstructures and defects in the shock-to-deflagration transition, through particle-based simulation.

The discovery of materials with improved properties can be accelerated by models with the ability to combine data from multiple experimental information sources. A recurring task in the toolbox of practitioners is to map input physical descriptors to output properties of interest. Typically, both the outputs and many of the inputs are experimentally measured and, thus, noisy. Probabilistic regression methods, e.g., Gaussian process regression, can easily deal with noisy outputs, even if the noise is input-dependent. However, most regression methods cannot process noisy inputs. Ignoring input uncertainty leads to inaccurate predictive uncertainty, a crucial ingredient for the sequential design of experiments. The objective of this paper is to develop a regression methodology that can deal with input uncertainty when one wishes to correlate an inexpensive experimental measurement (e.g., hardness) to an expensive one (e.g., yield strength). Our hierarchical Bayesian approach uses two Gaussian processes, forming a nested model. We demonstrate the merits of the proposed method through a synthetic dataset and apply it to predicting the yield strength of high entropy alloys from hardness on an exhaustive dataset compiled from the available literature.

Active learning (AL) can drastically accelerate materials discovery; its power has been shown in various classes of materials and target properties. Prior efforts have used machine learning models for the optimal selection of physical experiments or physics-based simulations. However, the latter efforts have been mostly limited to the use of electronic structure calculations and properties that can be obtained at the unit cell level and with negligible noise. We couple AL with molecular dynamics simulations to identify multiple principal component alloys (MPCAs) with high melting temperatures. Building on cloud computing services through nanoHUB, we present a fully autonomous workflow for the efficient exploration of the high dimensional compositional space of MPCAs. We characterize how uncertainties arising from the stochastic nature of the simulations and the acquisition functions used to select simulations affect the convergence of the approach. Interestingly, we find that relatively short simulations with significant uncertainties can be used to efficiently find the desired alloys as the random forest models used for AL average out fluctuations.

The shock to detonation transition in heterogeneous plastically bonded explosives is dominated by energy localization into hotspots that arise from the interaction of the shockwave with microstructural features and defects. The complex polycrystalline structure of these materials leads to a network of hotspot that can coalesce into deflagration and detonation waves. Significant progress has been made on the formation and potency of hotspots using atomistic simulations, but most of the work has focused on ideal and isolated defects. Hence, developed a method, denoted PBXGen, to build realistic PBX microstructures for all-atom simulations. PBXGen is generally applicable, and we demonstrate it with two systems: an RDX-polystyrene PBX with a 3D microstructure and a TATB-polystyrene with columnar grains. The resulting structure exhibit key features of PBXs, albeit at smaller scales, and are validated against experimental mechanical and shock properties.

Regions of energy localization referred to as hotspots are known to govern shock initiation and the run-to-detonation in energetic materials. Mounting computational evidence points to accelerated chemistry in hotspots from large intramolecular strains induced via the interactions between the shock wave and microstructure. However, definite evidence mapping intramolecular strain to accelerated or altered chemical reactions has so far been elusive. From a large-scale reactive molecular dynamics simulation of the energetic material 1,3,5-triamino-2,4,6-trinitrobenzene, we map decomposition kinetics to molecular temperature and intramolecular strain energy prior to reaction. Both temperature and intramolecular strain are shown to accelerate chemical kinetics. A detailed analysis of the atomistic trajectory shows that intramolecular strain can induce a mechanochemical alteration of decomposition mechanisms. The results in this paper could inform continuum-level chemistry models to account for a wide range of mechanochemical effects.

In energetic materials, the localization of energy into "hotspots" is known to dictate the initiation of chemical reactions and detonation. Recent all-atom simulations have shown that more energy is localized as internal potential energy (PE) than can be inferred from the kinetic energy (KE) alone. The mechanisms associated with pore collapse and hotspot formation are known to depend on pore geometry and dynamic material response such as plasticity. Therefore, we use molecular dynamics (MD) simulations to characterize shock-induced pore collapse and the subsequent formation of hotspots in 1,3,5-triamino-2,4,6-trinitrobenzene (TATB), a highly anisotropic molecular crystal, for various defect shapes, shock strengths, and crystallographic orientations. We find that the localization of energy as PE is consistently larger than the KE in cases with significant plastic deformation. An analysis of MD trajectories reveals the underlying molecular- and crystal-level processes that govern the effect of orientation and pore shape on PE localization. We find that the regions of highest PE relate to the areas of maximum plastic deformation, while KE is maximized at the point of impact. Comparisons against octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) reveal less energy localization in TATB, which could be a contributing factor to the latter's insensitivity.

2D rare-earth metal carbides (MXenes) are attractive due to their novel electronic and magnetic properties and their potential as scalable 2D magnets. In this study, we used density functional theory with the Hubbard U correction to characterize the structure, termination, and magnetism in an out-of-plane ordered rare-earth containing M3C2T x MXene, Mo2NdC2T2 (T = O or OH). We investigated the effect of the U parameter on the stability and magnetism of two possible termination sites: the hollow sites aligned with the inner Nd atoms (Nd-hollow sites) and those aligned with the closest C atoms (C-hollow sites). We found that increasing UMo stabilized the Nd hollow sites, which minimized electrostatic repulsion between C and O atoms. Using UMo = 3.0 eV and UNd = 5.6 eV, obtained via the linear response method, we found that the energetically preferred termination site was C-hollow in Mo2NdC2O2 and Nd-hollow in Mo2NdC2(OH)2. Regardless of termination and the Hubbard U value, we found Mo2NdC2O2 and Mo2NdC2(OH)2 to be magnetic. The C-hollow termination resulted in ferromagnetic states for all Hubbard U tested with no magnetic moment in Mo. In the case of Nd-hollow, Mo became magnetic for UMo ≥ 4 eV. The difference of Mo magnetism in Nd-hollow and C-hollow was explained by crystal field splitting of the Mo d orbital caused by a distorted ligand.

Refractory complex concentrated alloys (RCCAs) are a relatively new class of materials that can exhibit excellent mechanical properties at high temperatures, and determining their melting temperature ( Tm) is critical to assess their range of operation. Unfortunately, the experimental determination of this property is challenging and computational tools to predict the Tm of RCCAs from first-principles calculations are highly desirable. We quantify the uncertainties associated with such predictions for two methods that can be used with density functional theory-based molecular dynamics and apply them to predict the melting temperature of equiatomic NbMoTaW. We find that a combination of free energy calculations of individual phases with a dynamical coexistence method can provide accurate results with the minimum possible computational cost. We predict the melting temperature for the RCCA NbMoTaW to be between 3000 and 3100 K.

We use molecular dynamics simulations to characterize the shock-induced collapse of porosity of 1,3,5,7-tetranitro-1,3,5,7-tetrazoctane. We focus on how pore size and shape affect the collapse mechanism and resulting hotspot temperature distribution. Within the hydrodynamic collapse regime, for particle velocities above 0.7 km/s, we find that a combination of the curvature of the downstream surface and void length affects the terminal velocity of the expanding material and, consequently, temperature. Increasing curvature and length result in faster speeds, including jetting, and higher temperatures. For long and thin voids, there is a maximum in temperature with curvature as lateral collapse restricts the expanding material. The simulations map void size and shape to the resulting hotspot and provide a key piece of information toward the development of predictive modeling of shock-induced initiation.

The relaxation of polymers around and below their glass transition temperature is governed by a range of correlated unit processes with a wide range of timescales. The fast deformation rates of shock loading can suppress a significant fraction of these processes resulting in dynamical glass transition in rubbers. In this article, we report the inverse, a transient melting of a glassy polymer under shock loading. The large deviatoric stresses near the shock front induce fast transitions in backbone dihedral angles and a stress relaxation characteristic of polymer melts. This is followed by slower relaxation expected for glasses.

Complex-concentrated-alloys (CCAs) are of interest for a range of applications due to a host of desirable properties, including high-temperature strength and tolerance to radiation damage. Their multi-principal component nature results in a vast number of possible atomic environments with the associated variability in chemistry and structure. This atomic-level variability is central to the unique properties of these alloys but makes their modeling challenging. We combine atomistic simulations using many body potentials with machine learning to develop predictive models of various atomic properties of CrFeCoNiCu-based CCAs: relaxed vacancy formation energy, atomic-level cohesive energy, pressure, and volume. A fingerprint of the local atomic environments is obtained combining invariants associated with the local atomic geometry and periodic-table information of the atoms involved. Importantly, all descriptors are based on the unrelaxed atomic structure; thus, they are computationally inexpensive to compute. This enables the incorporation of these models into macroscopic simulations. The models show good accuracy and we explore their ability to extrapolate to compositions and elements not used during training.

We characterize the role of coherent nanoscale B2 Ni50Al50 precipitates on the temperature- and stress-induced martensitic phase transformation in nanocrystalline Ni63Al37 shape memory alloys using multi-million-atoms molecular dynamics (MD) simulations. We studied two types of precipitates: one with single crystal precipitates (SXP) and a second where grain boundaries cut through precipitates (PXP). Simulations reveal that the presence of B2 precipitates stabilizes the cyclic flag-shaped stress-strain response, characteristic of superelasticity, and reduces remnant strain. In contrast, single-phase nanocrystalline Ni63Al37 exhibits degradation of the reverse transformation during cyclic loading and, eventually, incomplete reversible transformation within a few cycles. This is consistent with previous experimental findings of ultra-low fatigue in Ni-Ti-Cu alloys with Ti2Cu precipitates. The simulations reveal that the presence of precipitates significantly improves the reversibility of the transformation by acting as elastic zones that partially shield the martensitic transformation and drive the reverse transformation.

Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science and engineering workflows used in research remain poorly documented and often unavailable, involving ad hoc scripts and manual steps, hindering reproducibility and stifling progress. We introduce Sim2Ls (pronounced simtools) and the Sim2L Python library that allow developers to create and share end-to-end computational workflows with well-defined and verified inputs and outputs. The Sim2L library makes Sim2Ls, their requirements, and their services discoverable, verifies inputs and outputs, and automatically stores results in a globally-accessible simulation cache and results database. This simulation ecosystem is available in nanoHUB, an open platform that also provides publication services for Sim2Ls, a computational environment for developers and users, and the hardware to execute runs and store results at no cost.

Materials with higher operating temperatures than today's state of the art can improve system performance in several applications and enable new technologies. Under most scenarios, a protective oxide scale with high melting temperatures and thermodynamic stability as well as low ionic diffusivity is required. Thus, the design of high-temperature systems would benefit from knowledge of these properties and related ones for a large number of oxides. While some properties of interest are available for many oxides (e.g., elastic constants exist for > 1000 oxides), the melting temperature is known for a relatively small subset. The determination of melting temperatures is time consuming and costly, both experimentally and computationally; thus, we use data science tools to develop predictive models from the existing data. Since the relatively small number of available melting temperature values precludes the use of standard tools, we use a multi-step approach based on transfer learning where surrogate data from first principles calculations are leveraged to develop models using small datasets. We use these models to predict the desired properties for nearly 11,000 oxides and quantify uncertainties in the space.

Shock initiation of heterogeneous high-energy density materials is mediated by the formation of hotspots, and the collapse of porosity is considered the dominant mechanism behind energy localization. This is particularly important in emerging amorphous energetics yet little is known about how the intrinsic properties of glasses affect the size, shape, and temperature of hotspots in these materials. Therefore, we use large-scale molecular dynamics simulations to characterize hotspot formation in amorphous 1,3,5,7-tetranitro-1,3,5,7-tetrazoctane originating from the collapse of cylindrical voids over a range of shock strengths. We find a transition from a viscoplastic to a hydrodynamic regime with increasing shock strength, similar to what is observed in the crystalline case. Interestingly for weak shocks, viscoplastic collapse in the amorphous system results in higher hotspot temperatures than in the crystal; this difference originates from the lower strength in the amorphous samples which results in faster collapse. On the other hand, in the hydrodynamic regime, where strength does not dominate the process of collapse, the hotspot temperature in the amorphous case is lower. The simulations reveal the molecular origin for these observations.

Electronic structure calculations of 4H-SiC doped with various transition metals reveal dilute magnetic semiconductor behavior in a material suitable for high-power and high-frequency semiconductor devices. Our results are consistent with prior work on V, Cr, and Mn doping and explore additional metals: Fe, Co, and Ni. Charge-state calculations show that the latter maintain amphoteric semi-insulating properties while offering a non-zero stable spin polarization and also greater asymmetry in the spin density of states than previously studied dopants. This indicates possible enhanced half-metal properties. Our results are consistent with crystal field theory, which helps interpret the observed spin states and assess the degree of charge localization and, subsequently, the range and strength of interactions relevant to ionization/capture and charge transport. These findings provide new avenues to tune the behavior of 4H-SiC for electronic device applications.

We use density functional theory to investigate the possibility of polar and multiferroic states in free-standing, perovskite-based nanodots at the atomic limit of miniaturization: single unit cells with terminations which allow centro-symmetry. We consider both A-O and B-O2 terminations for three families of nanodots: (i) A = Ba with B = Ti, Zr, and Hf; (ii) A = Ca and Sr with B = Ti; and (iii) A = Na and K with B = Nb. We find all A–O terminated dots to be non-polar and to exhibit cubic symmetry (except for K8NbO6), regardless of the presence of ferroelectricity in the bulk. In contrast, all the B–O2 terminated nanodots considered relax to a non-cubic ground state. Rather surprisingly, all of these structures exhibit polar ground states (except NaNb8O12). We propose a new structural parameter, the cluster tolerance factor (CTF), to determine whether a particular chemistry will result in a polar ground state nanodot, analogous to the Goldschmidt factor for bulk ferroelectrics. In addition, we find that all A–O terminated (except Ca8TiO6) and all polar B–O2 terminated nanodots are magnetic, where none show magnetism in the bulk. As with bulk systems, multiferroicity in the B–O2 terminated dots originates from separation between spin density in peripheral B atoms and polarity primarily caused by the off-center central A atom. Our findings stress that surface termination plays a crucial role in determining whether ferroelectricity is completely suppressed in perovskite-based materials at their limit of miniaturization.

Gallium nitride (GaN) and AlxGa1−xN, its solid solution with Al, play a vital role in a variety of high-power applications owing to their high breakdown voltage, drift velocity, and sheet charge density. Their piezoelectric nature is critical for both the operation and reliability of GaN-based devices, and this is compounded by the lack of lattice-matched substrates and the lattice mismatch between GaN and AlxGa1−xN, which invariably results in strained films. Thus, accurate models of performance and reliability require knowledge of how strain affects dielectric and piezoelectric response. We used density functional theory to calculate the piezoelectric and dielectric constants for different compositions of AlxGa1−xN as a function of biaxial strain and use Gaussian process regression to develop models, including uncertainties, from the ab initio results. We find that the dielectric constants decrease with compressive biaxial strain and increasing Al content due to an increase in phonon frequencies. Meanwhile, the piezoelectric constants increase with compressive biaxial strain and with Al doping. Our results show that the presence of strain can explain discrepancies in experimental measurements of dielectric constants but not piezoelectric ones. Interestingly, the piezoelectric constants e33 and e31 (which control the elastic energy induced by the application of gate voltage in GaN high electron mobility transistors, which have been related to their degradation) vary by almost 100% within a biaxial strain range of ∼3%. These results indicate that incorporating strain-dependent and composition-dependent piezoelectric response into current degradation models based on inverse piezoelectricity is crucial for accurate reliability predictions in GaN-based transistors.

Reactive force fields have enabled an atomic level description of a wide range of phenomena, from chemistry at extreme conditions to the operation of electrochemical devices and catalysis. While significant insight and semi-quantitative understanding have been drawn from such work, the accuracy of reactive force fields limits quantitative predictions. We developed a neural network reactive force field (NNRF) for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine (RDX). NNRF was trained using energies and forces of a total of 3100 molecules (11,941 geometries) and 15 condensed matter systems (32,973 geometries) obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions. The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained. The root mean square (RMS) error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials.

The collapse of pores plays an essential role in the shock initiation of high energy (HE) materials. When these materials are subjected to shock loading, energy is localized in hot-spots due to various mechanisms, including void collapse. Depending on the void size and shock strength, the resulting hot-spots may quench or evolve into a self-sustained deflagration wave that consequently can cause detonation. We compare finite element (FE) and non-reactive molecular dynamic (MD) simulations to study the formation of hot-spots during the collapse of an 80 nm size void in a β-tetramethylene tetranitramine energetic crystal. The crystal is shocked normal to the crystallographic plane ( 010 ), applying boundary velocities of 0.5 km/s, 1.0 km/s, and 2.0 km/s. The FE simulations capture the transition from viscoelastic collapse for relatively weak shocks to a hydrodynamic regime, the overall temperature distributions, especially at scales relevant for the initiation of HE materials, and the rate of pore collapse. A detailed comparison of velocity and temperature fields shows that the MD simulations exhibit more localization of plastic deformation, which results in higher temperature spikes but localized to small volumes. The void collapse rate and temperature field are strongly dependent on the plasticity model in the FE results, and we quantify these effects.

Shockwave interactions with a material's microstructure localizes energy into hotspots, which act as nucleation sites for complex processes such as phase transformations and chemical reactions. To date, hotspots have been described via their temperature fields. Nonreactive, all-atom molecular dynamics simulations of shock-induced pore collapse in a molecular crystal show that more energy is localized as potential energy (PE) than can be inferred from the temperature field and that PE localization persists beyond thermal diffusion. The origin of the PE hotspot is traced to large intramolecular strains, storing energy in modes readily available for chemical decomposition.

We characterize the atomic processes that underlie forming, reset, and set in HfO2-based resistive random access memory (RRAM) cells through molecular dynamics (MD) simulations, using an extended charge equilibration method to describe external electric fields. By tracking the migration of oxygen ions and the change in coordination of Hf atoms in the dielectric, we characterize the formation and dissolution of conductive filaments (CFs) during the operation of the device with atomic detail. Simulations of the forming process show that the CFs form through an oxygen exchange mechanism, induced by a cascade of oxygen displacements from the oxide to the active electrode, as opposed to aggregation of pre-existing oxygen vacancies. However, the filament breakup is dominated by lateral, rather than vertical (along the filament), motion of vacancies. In addition, depending on the temperature of the system, the reset can be achieved through a redox effect (bipolar switch), where oxygen diffusion is governed by the applied bias, or by a thermochemical process (unipolar switch), where the diffusion is driven by temperature. Unlike forming and similar to reset, the set process involves lateral oxygen atoms as well. This is driven by field localization associated with conductive paths.

Shock loading takes materials from ambient conditions to extreme conditions of temperature and nonhydrostatic stress on picosecond timescales. In molecular materials the fast loading results in tem...

Hybrid electrolyte materials comprising polymer-ionic salt matrixes embedded with garnet particles constitute a promising class of materials for the realization of all-solid-state batteries. In addition to providing solutions to the safety issues inherent to current liquid electrolytes, hybrid polymer electrolytes offer advantages over other solid-state electrolytes. This is because their functional properties such as ionic conductivity, electrochemical stability, and mechanical and thermal properties can be tailored to a particular application by independently optimizing the properties of the constituent materials. This independent optimization permits the rational design of solid-state electrolytes, thereby solving the current bottlenecks that prevent their practical implementation into battery devices. This Mini-Review starts with a survey of solid-state electrolytes, focusing on their materials and ion transport limitations. Next, we summarize the current understanding of transport mechanisms in composite polymer electrolytes (CPEs) with the purpose of identifying materials’ solutions for further improving their properties. The overall goal of the Mini-Review is to foster heightened research interest in these hybrid structures to rapidly advance development of future all-solid-state battery devices.

We explore the systematic construction of kinetic models from in silico reaction data for the decomposition of nitromethane. Our models are constructed in a computationally affordable manner by using reactions discovered through accelerated molecular dynamics simulations using the ReaxFF reactive force field. The reaction paths are then optimized to determine reaction rate parameters. We introduce a reaction barrier correction scheme that combines accurate thermochemical data from density functional theory with ReaxFF minimal energy paths. We validate our models across different thermodynamic regimes, showing predictions of gas phase CO and NO concentrations and high-pressure induction times that are similar to experimental data. The kinetic models are analyzed to find fundamental decomposition reactions in different thermodynamic regimes.

Machine learning is playing an increasing role in the physical sciences and significant progress has been made towards embedding domain knowledge into models. Less explored is its use to discover interpretable physical laws from data. We propose parsimonious neural networks (PNNs) that combine neural networks with evolutionary optimization to find models that balance accuracy with parsimony. The power and versatility of the approach is demonstrated by developing models for classical mechanics and to predict the melting temperature of materials from fundamental properties. In the first example, the resulting PNNs are easily interpretable as Newton’s second law, expressed as a non-trivial time integrator that exhibits time-reversibility and conserves energy, where the parsimony is critical to extract underlying symmetries from the data. In the second case, the PNNs not only find the celebrated Lindemann melting law, but also new relationships that outperform it in the pareto sense of parsimony vs. accuracy.

2,6-Diamino-3,5-dinitropyrazine-1-oxide (LLM-105) is a relatively new and promising insensitive high-explosive (IHE) material that remains only partially characterized. IHEs are of interest for a range of applications and from a fundamental science standpoint, as the root causes behind insensitivity are poorly understood. We adopt a multitheory approach based on reactive molecular dynamic simulations performed with density functional theory, density functional tight-binding, and reactive force fields to characterize the reaction pathways, product speciation, reaction kinetics, and detonation performance of LLM-105. We compare and contrast these predictions to 1,3,5-triamino-2,4,6-trinitrobenzene (TATB), a prototypical IHE, and 1,3,5,7-tetranitro-1,3,5,7-tetrazoctane (HMX), a more sensitive and higher performance material. The combination of different predictive models allows access to processes operative on progressively longer timescales while providing benchmarks for assessing uncertainties in the predictions. We find that the early reaction pathways of LLM-105 decomposition are extremely similar to TATB; they involve intra- and intermolecular hydrogen transfer. Additionally, the detonation performance of LLM-105 falls between that of TATB and HMX. We find agreement between predictive models for first-step reaction pathways but significant differences in final product formations. Predictions of detonation performance result in a wide range of values, and one-step kinetic parameters show the similar reaction rates at high temperatures for three out of four models considered.

Spherulites are the most ubiquitous of polycrystalline microstructure of polymers; they develop under a wide range of conditions by the subsequent branching of crystalline lamella that results in an overall spherical shape. Despite significant efforts over decades, the mechanisms behind branching remain unclear. Molecular dynamics simulations in polyethylene reveal the molecular-level origin of noncrystallographic branching and the initial formation of fibrils. We find that the growth of crystalline lamella by reeling in and folding of polymer chains causes surprisingly large local deformation which, in turn, aligns the chains in the neighboring undercooled liquid. Thus, subsidiary grains nucleate with preferred orientations resulting in fibril growth with branching at small angles, consistent with those observed experimentally.

Abstract This investigation explores the nucleonic thermal conductivity of nuclear star matter as it undergoes the “topological” transition to the “pasta” regime, and further down to the solid-liquid phase transition. The study was carried out using molecular dynamics simulations with nuclear potentials embedded in an effective (i.e. Thomas-Fermi) Coulomb potential. The nucleonic thermal conductivity experiences a dramatic change within a narrow temperature interval around T ≃ 1 MeV. This change accompanies the “pasta” breakdown during a heating process. The nucleonic thermal conductivity, by flipping protons' or neutrons' velocity, further shows a decoupling for asymmetric nuclear star matter.

As the challenges in continued scaling of the integrated circuit technology escalate every generation, there is an urgent need to find viable solutions for both the front-end-of-line (transistors) and the back-end-of-line (interconnects). For the interconnect technology, it is crucial to replace the conventional barrier and liner with much thinner alternatives so that the current driving capability of the interconnects can be maintained or even improved. Due to the inherent atomically thin body thicknesses, 2D materials have recently been proposed and explored as Cu diffusion barrier alternatives. In this Perspective article, a variety of 2D materials that have been studied, ranging from graphene, h-BN, MoS2, WSe2 to TaS2, will be reviewed. Their potentials will be evaluated based on several criteria, including fundamental material properties as well as the feasibility for technology integration. Using TaS2 as an example, we demonstrate a large set of promising properties and point out that there remain challenges in the integration aspects with a few possible solutions waiting for validation. Applications of 2D materials for other functions in Cu interconnects and for different metal types will also be introduced, including electromigration, cobalt interconnects, and radio-frequency transmission lines.

Energy localization in hotspots due to shock-induced pore collapse is thought to be a critical process in the initiation of heterogeneous high-energy density materials. The dynamical collapse of porosity involves expansion, jetting, shearing, and recompression of the material surrounding the defect. While the resulting hotspots are known to result in deflagration waves that can lead to detonation, we lack the understanding of the relative potency of the various processes that occur during the collapse. We use molecular dynamics simulations with the reactive force field ReaxFF to characterize how uniaxial expansion/recompression, shear, and combinations thereof affect the formation and criticality of hotspots in RDX, 1,3,5-trinitro-1,3,5-triazine. We chose a planar pore configuration consisting of a 40 nm gap and independently control the relative amounts of compressive and shear shock loadings. We find that shear-dominated critical hotspots tend to be smaller but exhibit higher temperatures than uniaxial ones and involve longer reaction time scales. Interestingly, the chemical decomposition mechanisms are affected by the relative amount of dynamical shear and uniaxial loads.

The shock to detonation transition in heterogeneous high energy density solids starts with the spatial localization of mechanical energy into so-called hotspots that form due to the interaction between the leading wave and microstructural features and defects. We used large-scale molecular dynamics to characterize the hotspots resulting from the shock-induced collapse of cylindrical voids and elongated cracks focusing on the effect of shock strength, defect shape, and size. The temperature fields resulting from the collapse of cracks elongated along the shock direction show significantly higher sensitivity to both shock strength and size than cylindrical voids. Cracks 80 nm in length result in temperatures almost three times higher than voids 80 nm in diameter, reaching values corresponding to the ideal case of isentropic recompression of a gas. The molecular dynamics trajectories reveal the atomic origin of this contrasting behavior. While circular voids undergo a transition from viscoelastic pore collapse to a hydrodynamic regime with increasing shock strength, shock focusing in elongated cracks results in jetting and vaporization which, upon recompression, leads to increased heating.

Glassy polymers are utilized in numerous applications ranging from light-weight structural materials to membranes for industrial gas separation. In this study, we quantify the ability of non-equilibrium molecular dynamics (NEMD) simulations to predict mechanical properties of glassy polymers based on different modeling approaches: force field selection, number of polymer chains in the simulation cell, and polymer builder algorithm. The polymers analyzed in this work are poly(methyl methacrylate) (PMMA), poly(propylene) (PP), and a polymer of intrinsic microporosity (PIM-1). PMMA samples were synthesized in silico using three different methods: continuous configurational biased Monte Carlo, pseudo-self-avoiding random walk, and a generalized simulated polymerization approach. Following the application of a consistent equilibration approach for each PMMA sample, stress–strain data from simulated tensile testing revealed that the choice of polymer builder algorithm or number of chains comprising the simulation cell did not have significant impact on the predicted elastic modulus. Force field selection effects have been analyzed by applying generalized force fields (GAFF and DREIDING) to each PMMA and PP sample and were found to be the most influencing factor studied. For these polymers, it was found that the DREIDING force field provides excellent agreement with experimental tensile moduli, ∼3.25 GPa and ∼1.53 GPa for PMMA and PP, respectively, while GAFF provides a systematic overestimation of the modulus by approximately 1.0 GPa. However, in the case of the PIM-1 model, with previously validated bonded interactions described by GAFF and non-bonded parameters from the TraPPE force field, the tensile modulus was predicted to be 1.23 GPa, which is well within the range of measured experimental values. Altogether, the simulations performed in this study illustrate the capabilities of atomistic MD simulations to predict the elastic modulus of glassy polymers and highlight energetic potential terms to consider for force field validation.

Thin films of pentaerythritol tetranitrate (PETN) were shock compressed using the laser driven shock apparatus at Los Alamos National Laboratory (LANL). Two spectroscopic probes were available to this apparatus: visible white light transient absorption spectroscopy (VIS) from 400-700 nm and mid-infrared transient absorption spectroscopy (MIR) from 1150-3800 cm-1. Important PETN vibrational modes are the symmetric and anti-symmetric NO2 stretches at 1280 and 1650 cm-1, respectively, as well as CH stretches at ~2900 cm-1. Shock strength was varied from approximately 3 to 55 GPa to span from the chemically unreactive regime to the regime in which fast chemical reaction took place on the 250 ps time scale of the measurements. VIS and MIR results suggest irreversible chemistry was induced in PETN at pressures above 30 GPa. At lower shock pressures, the spectroscopy showed minimal changes attributable to pressure induced effects. Under the higher-pressure reactive conditions, the frequency region at the anti-symmetric NO2 stretch mode had a significantly increased absorption while the region around the symmetric NO2 stretch did not. No observable increased absorption occurred in the higher frequency regions where CH-, NH-, and OH- bonds would be observed. A broad absorption appeared on the shoulder at the red-edge of the CO2 vibrational band around 2200 cm-1. In addition to the experiments, reactive molecular dynamics were carried out under equivalent shock conditions to correlate the evolution of the infrared spectrum to molecular processes. The simulations show results consistent to experiments up to 30 GPa, but suggest NO and NO2 related features provided the strongest contributions to the shocked infrared changes. Proposed mechanisms for shocked PETN chemistry are analyzed as consistent or inconsistent with the data presented here. Our experimental data suggests C≡O or N2O bond formation, nitrite formation, and absence of significant hydroxyl or amine concentrations in the initial chemistry steps in PETN shocked above 30 GPa.

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.

Abstract Mg-Sc shape memory alloys are attractive for a wide range of applications due to their low density. Unfortunately, the use of these alloys is hindered by a low martensitic transformation temperature (173 K). We used density functional theory to characterize the energetics associated with the martensitic transformation in a Mg-Sc (19.44 at.% Sc) alloy from a disordered body centered cubic (BCC) austenite to a disordered orthorhombic martensite. The simulations predict lattice parameters and diffraction patterns in good agreement with experiments and the martensite phase to be 11 ∓ 1 meV/atom lower in energy than austenite at zero temperature, consistent with the low martensitic transformation temperature. A local ordering analysis of various structures revealed the origin of stacking faults in the HCP ordering in the martensite phase. In addition, we explore the effect of epitaxial strain on the relative energy between the two phases with the objective of increasing the martensitic transformation temperature. Compressive strain along [100] and tensile strain along [ 0 1 ¯ 1 ] on the closest packed plane (011) stabilize the martensite phase with respect to austenite. Bi-axial strain between 5 and 7% increases the zero-temperature energy difference between the phases by over 60%. Similar stabilization of the martensite phase can be achieved by the addition of pure Mg as a coherent second phase. Superlattices with 50 at.% Mg result in an energy difference of 18.1 meV/atom between the two phases at zero temperature. These results indicate that coherency strains can be used to increase the martensitic transformation and operation temperature of Mg-Sc alloys to room temperature.

We explore the possibilities and limitations of using a coherent second phase to engineer the thermo-mechanical properties of a martensitic alloy by modifying the underlying free energy landscape that controls the transformation. We use molecular dynamics simulations of a model atomistic system where the properties of a coherent, nanoscale second phase can be varied systematically. With a base martensitic material that undergoes a temperature-induced transformation from a cubic austenite to a monoclinic martensite, the simulations show a significant ability to engineer the transformation temperatures, from a ~50% reduction to a ~200% increase, with 50 at. % of the cubic second phase. We establish correlations between the properties of the second phase and the transformation characteristics and microstructure, via the free energy landscape of the two-phase systems. Coherency stresses have a strong influence on the martensitic variants observed and can even cause the non-martensitic second phase to undergo a transformation. Reducing the stiffness of second phase increases the transformation strain and modifies the martensitic microstructure, increasing the volume fraction of the transformed material. This increase in transformation strain is accompanied by a significant increase in the Af and thermal hysteresis, while the Ms remains unaltered. Our findings on the tunability of martensitic transformations can be used for informed searches of second phases to achieve desired material properties, such as achieving room temperature, lightweight shape memory alloys.

While most phase transformations, e.g. ferroelectric or ferromagnetic, can be first or second order depending on external applied fields, martensitic transformations in metallic alloys are nearly universally first order. We demonstrate that epitaxial stress originating from the incorporation of a tailored second phase can modify the free energy landscape that governs the phase transition and change its order from first to second. High-fidelity molecular dynamics simulations show a remarkable change in the character of the martensitic transformation in Ni-Al alloys near the critical point. We observe the continuous evolution of the transformation order parameter and scaling with power-law exponents comparable to those in other ferroic transitions exhibiting critical behavior. Our theoretical work provides a foundation to recent experimental and computational results on martensites near critical points.

We use molecular dynamics simulations to characterize spatio-temporal, high-mobility domains in various bulk polymers, thin slabs, and isolated chains as liquid samples are cooled across the glass ...

The response of high-energy-density materials to thermal or mechanical insults involves coupled thermal, mechanical, and chemical processes with disparate temporal and spatial scales that no single model can capture. Therefore, we developed a multiscale model for 1,3,5-trinitro-1,3,5-triazinane, RDX, where a continuum description is informed by reactive and nonreactive molecular dynamics (MD) simulations to describe chemical reactions and thermal transport. Reactive MD simulations under homogeneous isothermal and adiabatic conditions are used to develop a reduced-order chemical kinetics model. Coarse graining is done using unsupervised learning via non-negative matrix factorization. Importantly, the components resulting from the analysis can be interpreted as reactants, intermediates, and products, which allows us to write kinetics equations for their evolution. The kinetics parameters are obtained from isothermal MD simulations over a wide temperature range, 1200-3000 K, and the heat evolved is calibrated from adiabatic simulations. We validate the continuum model against MD simulations by comparing the evolution of a cylindrical hotspot 10 nm in diameter. We find excellent agreement in the time evolution of the hotspot temperature fields both in cases where quenching is observed and at higher temperatures for which the hotspot transitions into a deflagration wave. The validated continuum model is then used to assess the criticality of hotspots involving scales beyond the reach of atomistic simulations that are relevant to detonation initiation.

Approximating the dynamics of atomic nuclei with classical equations of motion in molecular dynamics (MD) simulations causes an overprediction of the specific heat and omits zero-point energy which can have a significant effect on predictions of the response of materials under dynamical loading. We use quantum and classical thermostats in reactive MD simulations to characterize the effect of energy distribution on the initiation and decomposition of the explosive 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) under shock and thermal loading. Shock simulations using the multiscale shock technique (MSST) show that nuclear quantum effects not only increase the temperature rise during dynamical loading but also lower the shock temperature corresponding to the threshold for initiation of chemical reactions. The lower specific heat and presence of zero point energy contribute approximately equally to these effects. Thermal decomposition simulations show that nuclear quantum effects lower the activation barrier associated with reaction compared to classical simulations. Quite interestingly, comparing quantum and classical simulations as a function of average kinetic energy shows that classical baths result in faster kinetics as compared with quantum ones; we explore the molecular origins of this observation.

and uncertainty propagation in CALPHAD modeling used to describe thermodynamics and phase stability. They discuss both aleatoric and epistemic uncertainties in the construction of thermodynamic potentials and use a Bayesian approach for model selection. Both these papers highlight the importance of UQ within ICME efforts.

Functional uncertainty quantification (FunUQ) was recently proposed to quantify uncertainties in models and simulations that originate from input functions, as opposed to parameters. This paper extends FunUQ to quantify uncertainties originating from interatomic potentials in isothermal-isobaric molecular dynamics (MD) simulations and to the calculation of defect formation energies. We derive and verify a computationally inexpensive expression to compute functional derivatives in MD based on perturbation theory. We show that this functional derivative of the quantities of interest (average internal energy, volume, and defect energies in our case) with respect to the interatomic potential can be used to predict those quantities for a different interatomic potential, without re-running the simulation. The codes and scripts to perform FunUQ in MD are freely available for download. In addition, to facilitate reproducibility and to enable use of best practices for the approach, we created Jupyter notebooks to perform FunUQ analysis on MD simulations and made them available for online simulation in nanoHUB. The tool uses cloud computing resources and users can view, edit, and run end-to-end workflows from a standard web-browser without the need to download or install any software.

Molecular dynamics simulations reveal anomalous short- and medium-range ordering with increasing temperature in network-forming ionic liquids (NIL) consisting of alkyl-diammonium cations with long side chains of 6 carbon atoms and citrate anions (NIL 5-6). This effect is weaker, and only a short-range order is observed in equivalent systems with side chains shortened to 3 C atoms (NIL 5-3). The short-range ordering can be attributed to volume expansion during heating, but the intermediate range order requires volume expansion as well as an increase in temperature. We find that the cross (cation-anion) interactions are the major contributors to the observed trend and the development of complex 3D correlations in the two-particle correlation functions. The simulations suggest that the above phenomenon can be correlated to local trapping of cation molecules in a variety of configurations at lower temperatures where molecular shape distributions show great variability; as temperature increases, the distribution of molecular radii of gyration becomes narrower, enabling the increased ordering.

Milling and micronization are commonly used to reduce the particle size of active pharmaceutical ingredients and excipients. During these processes the materials are subjected to extensive deformation that may result in defect nucleation, polymorphic transformations, and amorphization. Current amorphization models require parameters that demand extensive number of experiments. We present a multiscale framework to predict mechanically induced amorphization without experimental information. The model requires as input only the molecular structure and starts with molecular dynamics simulations to determine elastic constants, melting temperature, crystal-amorphous interface energy, and the energy density difference between the amorphous and crystalline phases. This information is used in a phase field model that includes defect nucleation and solid state amorphization. At each scale, the components of the model are validated by performing simulations of sucrose, lactose, acetaminophen, and gamma-indomethacin. The multiscale framework is exercised to predict the response of two pharmaceutical compounds F1 and F2, without any experimental information. The model indicates that F1 is resistant to disorder while F2 tends to be amorphized, in agreement with the experimental results.

Simulation tools are playing an increasingly important role in materials science and engineering and beyond their well established importance in research and development, these tools have a significant pedagogical potential. We describe a set of online simulation tools and learning modules designed to help students explore important concepts in materials science where hands-on activities with high-fidelity simulations can provide insight not easily acquired otherwise. The online tools, which involve density functional theory and molecular dynamics simulations, have been designed with non-expert end-users in mind and only a few clicks are required to perform most simulations, yet they are powered by research-grade codes and expert users can access advanced options. All tools and modules are available for online simulation in nanoHUB.org and access is open and free of charge. Importantly, instructors and students do not need to download or install any software. The learning modules cover a range of topics from electronic structure of crystals and doping, plastic deformation in metals, and physical properties of polymers. These modules have been used in several core undergraduate courses at Purdue’s School of Materials Engineering, they are self contained, and are easy to incorporate into existing classes.

We use molecular dynamics simulations to characterize the effect of various surface terminations on phonon thermal transport in nanoscale Cu slabs. Specifically, we studied Cu slabs approximately 4 nm in thickness with atomistically flat (111) surfaces, slabs with ∼ 30 % surface vacancies to mimic atomic-level roughness, and slabs with a surface oxide. Motivated by recent experimental observations, we study the effect of capping these surfaces with single layer graphene. From the thermal conductivity of the various samples as a function of length, we extracted conductivity and phonon mean free paths in the absence of boundary scattering other than that originating from the surfaces under study. As expected, both surface vacancies and an oxide layer reduce thermal conductivity and we characterize the effect in terms of the specularity parameter. While capping the slabs with graphene increases the conductivity, the poor thermal contact between Cu and graphene results in less than ideal performance of the hybrid material. Interestingly, the simulations reveal that the graphene capping layer reduces surface scattering on the Cu slabs, and this effect is significantly more pronounced in the case of a defective surface. The results provide insights into the use of graphene capping to improve transport in nanoscale interconnects for nanoelectronics.

Abstract Semicrystalline polyetherketoneketone (PEKK) is widely used as the matrix in carbon-fiber composites. Understanding and predicting the crystallization thermodynamics and kinetics of this class of polymers is, thus, of great interest. This paper uses molecular dynamic (MD) simulations using Dreiding united-atom (UA) potentials to characterize a wide range of thermos-physical properties of PEKK. We characterized the effect of terephthaloyl chloride to isophthaloyl chloride (T/I) on an extensive set of properties, including the lattice parameters and stability of PEKK crystal structures, glass transition temperature, melting temperature, crystal/amorphous interfacial energy, and enthalpy of fusion. We find good overall agreement between predicted properties and experimental values and the simulations help clarify inconsistencies in the literature. In combination with classical nucleation theory, nucleation barriers and critical nucleus size at different temperature are predicted.

Metal-insulator transitions in two dimensional materials represent a great opportunity for fast, low energy and ultra-dense switching devices. Due to the small energy difference between its semimetallic and semiconducting crystal phases, phase transition in MoTe$_2$ can occur with an unprecedented small amount of external perturbations. In this work, we used density functional theory to predict critical strain and electrostatic voltage required to control the phase transition of 3d and 4d metal doped MoTe$_2$. We found that small doping contents dramatically affect the relative energies of MoTe$_2$ crystal phases and can largely reduced the energy input to trigger the transition, compared to pristine case. Moreover, the kinetics corresponding to the phase transition in the proposed doped materials are several order of magnitude faster than in MoTe$_2$. For example, we predict 6.3 \% Mn doped MoTe$_2$ to switch phase under 1.19 V gate voltage in less than 1 $\mu$s with an input energy of 0.048 aJ/nm$^3$. Due to the presence of dopant, the controlled change of phase is often complemented with a change in magnetic moment leading to multi-functional phase transition.

We use molecular dynamics (MD) simulations with the ReaxFF reactive force field to investigate the thermomechanical, chemical, and spectroscopic response of nitromethane (NM) to shock loading. We simulate shocks using the Hugoniostat technique and use four different parametrizations of ReaxFF to assess the sensitivity of the results with respect to the force field. The predicted shock states, for both the unreacted and reacted materials, are in good agreement with experiments, and two of the force fields capture the increase in shock velocity due to exothermic reactions in excellent agreement with experiments. The predicted detonation velocities with these two force fields are also in good agreement with experiments, and the differences in predicted values are linked to the differences in the reaction products. Across all force fields, NM decomposes predominantly via bimolecular reactions and the formation of nitrosomethane (CH3NO) is found as a dominant initiation pathway. We elucidate the mechanisms of ...

Using first principles calculations, the use of strain to adjust electronic transport and the resultant thermoelectric (TE) properties is discussed using 2H phase CuAlO2 as a test case. Transparent oxide materials, such as CuAlO2, a p-type transparent conducting oxide (TCO), have recently been studied for high temperature thermoelectric power generators and coolers for waste heat. Given TCO materials with relative ease of fabrication, low cost of materials, and non-toxicity, the ability to tailor them to specific temperature ranges, power needs, and size requirements, through the use of strain opens an interesting avenue. We find that strain can have a significant effect on these properties, in some cases detrimental and in others beneficial, including the potential for n-type power factors larger than the highest p-type case. The physical reasons for this behavior are explained in the terms of the thermoelectric transport distribution and the Landauer distribution of modes.

Shock initiation of heterogeneous high-energy materials is often preceded by the loss of crystalline order around hotspots where mechanical energy is localized and chemical reactions start. We use molecular dynamics (MD) simulations with the reactive force field ReaxFF to determine the impact of molecular disorder on the reactivity of the high-energy material RDX under fast homogeneous heating and hotspots. Under fast heating to identical temperatures, amorphous samples exhibit faster decomposition and reaction than their crystalline counterparts. Following heating, the crystalline samples undergo fast endothermic processes associated with the loss of crystalline order that occur in timescales shorter than chemical decomposition and reduce the actual temperature of the reaction. Once this process is accounted for and actual decomposition temperatures are determined, both amorphous and crystalline samples follow identical kinetics. We also characterize the critical temperature required for a hotspot 10 nm in diameter to become critical and turn into a deflagration wave. In both crystalline and amorphous samples, hotspots with initial temperatures of 1650 K and higher result in self-sustained deflagration waves and those at 1600 K quench. We observe slightly faster propagation in the amorphous samples with initial velocities increasing with temperature. The higher reactivity of amorphous samples is not large enough to explain the significantly increased reactivity in hotspots formed after shock-induced pore collapse observed recently in large-scale MD simulations.

Abstract We develop a model to describe reaction-induced phase separation in thermoplastic-toughened thermoset polymers at molecular scales. To achieve the temporal and spatial scales required for phase separation we use coarse-grained molecular dynamics where beads represent bi-functional epoxy, a di-amine crosslinker and monomers in the thermoplastic. The model describes the curing of the thermoset using a distance criterion to identify chemical reactions on the fly during a molecular dynamics simulation. We characterize how composition, curing temperature and conversion degree affect the onset of phase separation and domain growth. The onset of phase separation in terms of degree of cure depends on cure temperature and the subsequent growth of domain size during cure can be described with two power laws. Interestingly, the domain size vs. time following quenching after cure also follows power-law behavior but with exponent of approximately ¼, lower than those observed in simple binary mixtures and linear chain polymers.

Abstract The molecular structure of spun polyacrylonitrile (PAN) fibers used in the production of carbon fibers (CFs) is known to critically affect the microstructure and, consequently, the properties of the final fiber. We use molecular dynamics (MD) simulations to predict the molecular structure of the crystalline regions of spun PAN. We characterized how tacticity and the arrangement of torsional angles along the backbone affect packing of the chains and lattice parameters. Most configurations, regardless of tacticity, loose periodicity along the chain axis during relaxation resulting in pseudo-crystalline structures. Simulated X-ray diffraction (XRD) patterns of these pseudo-crystalline structures show excellent agreement with recent experimental measurements and reveal that intermolecular spacing decreases as backbone trans/gauche ratio increases corresponding to different experimental conditions. Stability and stiffness also increase as backbone trans/gauche ratio increases. A syndiotactic system with planar zig-zag configuration results in crystalline order along the c axis; this more ordered structure has the lowest potential energy and highest stiffness of all structures studied. The predicted XRD pattern differs significantly from those of the pseudo-crystalline structures but, interestingly, it matches the multi-peak fingerprint reported experimentally in solution-grown single crystals of PAN. The simulations shed light into long-standing discrepancies in XRD patters of PAN precursors.

Electric double layer (EDL) dynamics in graphene field-effect transistors (FETs) gated with polyethylene oxide (PEO)-based electrolytes are studied by molecular dynamics (MD) simulations from picoseconds to nanoseconds and experimentally from microseconds to milliseconds. Under an applied field of approximately mV/nm, EDL formation on graphene FETs gated with PEO:CsClO4 occurs on the timescale of microseconds at room temperature and strengthens within 1 ms to a sheet carrier density of nS ≈ 1013 cm-2. Stronger EDLs (i.e., larger nS) are induced experimentally by pulsing with applied voltages exceeding the electrochemical window of the electrolyte; electrochemistry is avoided using short pulses of a few milliseconds. Dynamics on picosecond to nanosecond timescales are accessed using MD simulations of PEO:LiClO4 between graphene electrodes with field strengths of hundreds of mV/nm which is 100× larger than experiment. At 100 mV/nm, EDL formation initiates in sub-nanoseconds achieving charge densities up to 6 × 1013 cm-2 within 3 nanoseconds. The modeling shows that under sufficiently high electric fields, EDLs with densities ∼1013 cm-2 can form within a nanosecond, which is a timescale relevant for high-performance electronics such as EDL transistors (EDLTs). Moreover, the combination of experiment and modeling shows that the timescale for EDL formation ( nS = 1013 to 1014 cm-2) can be tuned by 9 orders of magnitude by adjusting the field strength by only 3 orders of magnitude.

Recrystallization of glasses is important in a wide range of applications including electronics and reactive materials. Molecular dynamics (MD) has been used to provide an atomic picture of this process, but prior work has neglected the thermal transport role of electrons, the dominant thermal carrier in metallic systems. We characterize the role of electronic thermal conductivity on the velocity of recrystallization in Ni using MD coupled to a continuum description of electronic thermal transport via a two-temperature model. Our simulations show that for strong enough coupling between electrons and ions, the increased thermal conductivity removes the heat from the exothermic recrystallization process more efficiently, leading to a lower effective temperature at the recrystallization front and, consequently, lower propagation velocity. We characterize how electron-phonon coupling strength and system size affect front propagation velocity. Interestingly, we find that initial recrystallization velocity increases with decreasing system size due to higher overall temperatures. Overall, we show that a more accurate description of thermal transport due to the incorporation of electrons results in better agreement with experiments.

Abstract Molecular dynamics simulations show that coherent precipitates can significantly affect the properties of martensitic transformations in Ni63Al37 alloys. The precipitates, consisting of non-martensitic Ni50Al50, modify the free energy landscape that governs the phase transformation and result in a significant reduction of the thermal hysteresis, at comparably minor expense of transformation strain, and modification of transformation temperatures. Importantly, this paper shows that free energy landscape engineering is possible with nanostructures potentially accessible through standard metallurgical processing routes. The atomistic-level nucleation and transformation mechanisms within the nanoprecipitate systems are explored and compared with epitaxial nanolaminates and nanowires. The simulations reveal three distinct regimes of transformation mechanisms and martensitic nanostructure as a function of volume fraction of the non-martensitic phase. Free energy landscape engineering is generally applicable and could contribute to the design of new shape memory alloys with novel properties, such as light weight alloys that operate at room temperature.

We quantify the effect of various sources of uncertainties in the prediction of thermo-physical properties of polymers using molecular dynamics simulations. We quantify how the choice of polymer builder, force field, molecular weight and data analysis affect predicted values of the glass transition temperature (Tg), room temperature density and coefficient of thermal expansion of poly(methyl-methacrylate) (PMMA) and polystyrene (PS). Interestingly, we find that the data analysis introduces significant uncertainties in Tg (approximately 5%) while the other properties are insensitive to it. The force field is the only variable that significantly affects the predictions of density. Polymer-consistent force field (PCFF) resulted in a higher density for PMMA than Dreiding and the opposite trend was observed in PS; in all cases the difference in density was less than 2%. Strongly correlated with density, we find that PCFF leads to a higher Tg than Dreiding for PMMA and both force fields predict similar Tg values for PS. The trends in Tg can be explained by differences in segmental mobility of the melts predicted by the two force fields. We find that the choice of amorphous polymer builder results in uncertainties in predictions comparable to those associated with the force field due to subtle, but persistent, differences in molecular structure. The results presented here provide insight into the physics behind molecular simulations of polymers and quantitative levels of uncertainties associated with individual sources that can help practitioners of molecular simulations interested in using their results in engineering applications.

This chapter presents a review and tutorial on molecular dynamics (MD) simulation of shock loading of solids, under which materials are compressed at ultra-fast rates to extreme conditions of pressure and temperature. Due to the ultra-fast loading rates, shockwaves can reveal processes not accessible otherwise, including melting below the equilibrium melting temperature and chemical reactions away from equilibrium. The timescales involved in shock physics make MD an ideal tool for their study and these atomic-level simulations have and continue to play a critical role of our understanding of the physics and chemistry of materials under extreme mechanical loads. Such simulations, when done with care, have been particularly useful to resolve complex processes that occur at or right behind the shock front, where plasticity, phase transformations, and the initiation of chemical reactions are relevant. Flyer plate simulations, coarse grain dynamics, and shock-induced plasticity are also discussed.

Abstract We use functional, Frechet, derivatives to quantify how thermodynamic outputs of a molecular dynamics (MD) simulation depend on the potential used to compute atomic interactions. Our approach quantifies the sensitivity of the quantities of interest with respect to the input functions as opposed to its parameters as is done in typical uncertainty quantification methods. We show that the functional sensitivity of the average potential energy and pressure in isothermal, isochoric MD simulations using Lennard–Jones two-body interactions can be used to accurately predict those properties for other interatomic potentials (with different functional forms) without re-running the simulations. This is demonstrated under three different thermodynamic conditions, namely a crystal at room temperature, a liquid at ambient pressure, and a high pressure liquid. The method provides accurate predictions as long as the change in potential can be reasonably described to first order and does not significantly affect the region in phase space explored by the simulation. The functional uncertainty quantification approach can be used to estimate the uncertainties associated with constitutive models used in the simulation and to correct predictions if a more accurate representation becomes available.

We characterize the energetics and atomic structures involved in the intercalation of copper and silver into the van der Waals gap of molybdenum disulfide as well as the resulting ionic and electronic transport properties using first-principles density functional theory. The intercalation energy of systems with formula (Cu,Ag)xMoS2 decreases with ion concentration and ranges from 1.2 to 0.8 eV for Cu; Ag exhibits a stronger concentration dependence from 2.2 eV for x = 0.014 to 0.75 eV for x = 1 (using the fcc metal as a reference). Partial atomic charge analysis indicates that approximately half an electron is transferred per metallic ion in the case of Cu at low concentrations and the ionicity decreases only slightly with concentration. In contrast, while Ag is only slightly less ionic than Cu for low concentrations, charge transfer reduces significantly to approximately 0.1 e for x = 1. This difference in ionicity between Cu and Ag correlates with their intercalation energies. Importantly, the predicted...

Alloy and microstructure optimization have led to impressive improvements in the strength of engineering metals, while the range of Young’s moduli achievable has remained essentially unchanged. This is because stiffness is insensitive to microstructure and bounded by individual components in composites. Here we design ultra-low stiffness in fully dense, nanostructured metals via the stabilization of a mechanically unstable, negative stiffness state of a martensitic alloy by its coherent integration with a compatible, stable second component. Explicit large-scale molecular dynamics simulations of the metamaterials with state of the art potentials confirm the expected ultra-low stiffness while maintaining full strength. We find moduli as low as 2 GPa, a value typical of soft materials and over one order of magnitude lower than either constituent, defying long-standing composite bounds. Such properties are attractive for flexible electronics and implantable devices. Our concept is generally applicable and could significantly enhance materials science design space. The rule of mixtures usually causes composite properties to fall between the maximum and minimum of the parent phases. Here, the authors use large-scale molecular dynamics simulations to break that rule by stabilizing a negative stiffness state in fully dense nickel-aluminum nanowires to achieve ultra-low stiffness.

Successful doping of single-layer transition metal dichalcogenides (TMDs) remains a formidable barrier to their incorporation into a range of technologies. We use density functional theory to study doping of molybdenum and tungsten dichalcogenides with a large fraction of the periodic table. An automated analysis of the energetics, atomic and electronic structure of thousands of calculations results in insightful trends across the periodic table and points out promising dopants to be pursued experimentally. Beyond previously studied cases, our predictions suggest promising substitutional dopants that result in p-type transport and reveal interesting physics behind the substitution of the metal site. Doping with early transition metals (TMs) leads to tensile strain and a significant reduction in the bandgap. The bandgap increases and strain is reduced as the d-states are filled into the mid TMs; these trends reverse as we move into the late TMs. Additionally, the Fermi energy increases monotonously as the ...

Layered transition metal dichalcogenides are emerging as key materials in nanoelectronics and energy applications. Predictive models to understand their growth, thermomechanical properties, and interaction with metals are needed in order to accelerate their incorporation into commercial products. Interatomic potentials enable large-scale atomistic simulations connecting first principle methods and devices. We present a ReaxFF reactive force field to describe molybdenum ditelluride and its interactions with copper. We optimized the force field parameters to describe the energetics, atomic charges, and mechanical properties of (i) layered MoTe2, Mo, and Cu in various phases, (ii) the intercalation of Cu atoms and small clusters within the van der Waals gap of MoTe2, and (iii) bond dissociation curves. The training set consists of an extensive set of first principles calculations computed using density functional theory (DFT). We validate the force field via the prediction of the adhesion of a single layer MoTe2 on a Cu(111) surface and find good agreement with DFT results not used in the training set. We characterized the mobility of the Cu ions intercalated into MoTe2 under the presence of an external electric field via finite temperature molecular dynamics simulations. The results show a significant increase in drift velocity for electric fields of approximately 0.4 V/Å  and that mobility increases with Cu ion concentration.

We use a particle-based mesoscale model that incorporates chemical reactions at a coarse-grained level to study the response of materials that undergo volume-reducing chemical reactions under shockwave-loading conditions. We find that such chemical reactions can attenuate the shockwave and characterize how the parameters of the chemical model affect this behavior. The simulations show that the magnitude of the volume collapse and velocity at which the chemistry propagates are critical to weaken the shock, whereas the energetics in the reactions play only a minor role. Shock loading results in transient states where the material is away from local equilibrium and, interestingly, chemical reactions can nucleate under such non-equilibrium states. Thus, the timescales for equilibration between the various degrees of freedom in the material affect the shock-induced chemistry and its ability to attenuate the propagating shock.

Our future engineers and scientists will likely be required to use advanced simulations to solve many of tomorrow's challenges in nanotechnology. To prepare students to meet this need, the Network for Computational Nanotechnology (NCN) provides simulation-focused research experiences for undergraduates at an early point in their educational path, to increase the likelihood that they will ultimately complete a doctoral program. The NCN summer research program currently serves over 20 undergraduate students per year who are recruited nationwide, and selected by NCN and the faculty for aptitude in their chosen field within STEM, as well as complementary skills such as coding and written communication. Under the guidance of graduate student and faculty mentors, undergraduates modify or build nanoHUB simulation tools for exploring interdisciplinary problems in materials science and engineering, and related fields. While the summer projects exist within an overarching research context, the specific tasks that NCN undergraduate students engage in range from modifying existing tools to building new tools for nanoHUB and using them to conduct original research. Simulation tool development takes place within nanoHUB, using nanoHUB’s workspace, computational clusters, and additional training and educational resources. One objective of the program is for the students to publish their simulation tools on nanoHUB. These tools can be accessed and executed freely from around the world using a standard web-browser, and students can remain engaged with their work beyond the summer and into their careers. In this work, we will describe the NCN model for undergraduate summer research. We believe that our model is one that can be adopted by other universities, and will discuss the potential for others to engage undergraduate students in simulation-based research using free nanoHUB resources.

We use density functional theory (DFT) to study the thermodynamic stability and migration of copper ions and small clusters embedded in amorphous silicon dioxide. We perform the calculations over an ensemble of statistically independent structures to quantify the role of the intrinsic atomic-level variability in the amorphous matrix affect the properties. The predicted formation energy of a Cu ion in the silica matrix is 2.7 ± 2.4 eV, significantly lower the value for crystalline SiO2. Interestingly, we find that Cu clusters of any size are energetically favorable as compared to isolated ions; showing that the formation of metallic clusters does not require overcoming a nucleation barrier as is often assumed. We also find a broad distribution of activation energies for Cu migration, from 0.4 to 1.1 eV. This study provides insights into the stability of nanoscale metallic clusters in silica of interest in electrochemical metallization cell memories and optoelectronics.

We introduce electrochemical dynamics with implicit degrees of freedom (EChemDID), a model to describe electrochemical driving force in reactive molecular dynamics simulations. The method describes the equilibration of external electrochemical potentials (voltage) within metallic structures and their effect on the self-consistent partial atomic charges used in reactive molecular dynamics. An additional variable assigned to each atom denotes the local potential in its vicinity and we use fictitious, but computationally convenient, dynamics to describe its equilibration within connected metallic structures on-the-fly during the molecular dynamics simulation. This local electrostatic potential is used to dynamically modify the atomic electronegativities used to compute partial atomic changes via charge equilibration. Validation tests show that the method provides an accurate description of the electric fields generated by the applied voltage and the driving force for electrochemical reactions. We demonstrate EChemDID via simulations of the operation of electrochemical metallization cells. The simulations predict the switching of the device between a high-resistance to a low-resistance state as a conductive metallic bridge is formed and resistive currents that can be compared with experimental measurements. In addition to applications in nanoelectronics, EChemDID could be useful to model electrochemical energy conversion devices.

Mesoscale phenomena--involving a level of description between the finest atomistic scale and the macroscopic continuum--can be studied by a variation on the usual atomistic-level molecular dynamics (MD) simulation technique. In mesodynamics, the mass points, rather than being atoms, are mesoscopic in size, for instance, representing the centers of mass of polycrystalline grains or molecules. In order to reproduce many of the overall features of fully atomistic MD, which is inherently more expensive, the equations of motion in mesodynamics must be derivable from an interaction potential that is faithful to the compressive equation of state, as well as to tensile de-cohesion that occurs along the boundaries of the mesoscale units. Moreover, mesodynamics differs from Newton's equations of motion in that dissipation--the exchange of energy between mesoparticles and their internal degrees of freedom (DoFs)--must be described, and so should the transfer of energy between the internal modes of neighboring mesoparticles. We present a formulation where energy transfer between the internal modes of a mesoparticle and its external center-of-mass DoFs occurs in the phase space of mesoparticle coordinates, rather than momenta, resulting in a Galilean invariant formulation that conserves total linear momentum and energy (including the energy internal to the mesoparticles). We show that this approach can be used to describe, in addition to mesoscale problems, conduction electrons in atomic-level simulations of metals, and we demonstrate applications of mesodynamics to shockwave propagation and thermal transport.

We characterize the electronic structure and elasticity of monolayer transition-metal dichalcogenides MX2 (M  =  Mo, W, Sn, Hf and X  =  S, Se, Te) based on 2H and 1T structures using fully relativistic first principles calculations based on density functional theory. We focus on the role of strain on the band structure and band alignment across the series of materials. We find that strain has a significant effect on the band gap; a biaxial strain of 1% decreases the band gap in the 2H structures, by as a much as 0.2  eV in MoS2 and WS2, while increasing it for the 1T cases. These results indicate that strain is a powerful avenue to modulate their properties; for example, strain enables the formation of, otherwise impossible, broken gap heterostructures within the 2H class. These calculations provide insight and quantitative information for the rational development of heterostructures based on this class of materials accounting for the effect of strain.

We study thermal transport through Pt nanowires that bridge planar contacts as a function of wire length and vibrational frequency of the contacts. When phonons in the contacts have lower average frequencies than those in the wires thermal transport occurs under conditions away from local equilibrium with low-frequency phonons experiencing a higher thermal gradient than high-frequency ones. This results in a size-dependent increase in the effective thermal conductivity of the wire with decreasing vibrational frequencies of the contacts. The interfacial resistivity when heat flows from the wire to the contact is also size-dependent and has the same physical origin in the lack of full equilibration in short nanowires. We develop a model based on a 1D atomic chain that captures the salient physics of the MD results.

Abstract We characterize how uncertainties propagate across spatial and temporal scales in a physics-based model of nanocrystalline plasticity of fcc metals. Our model combines molecular dynamics (MD) simulations to characterize atomic-level processes that govern dislocation-based-plastic deformation with a phase field approach to dislocation dynamics (PFDD) that describes how an ensemble of dislocations evolve and interact to determine the mechanical response of the material. We apply this approach to a nanocrystalline Ni specimen of interest in micro-electromechanical (MEMS) switches. Our approach enables us to quantify how internal stresses that result from the fabrication process affect the properties of dislocations (using MD) and how these properties, in turn, affect the yield stress of the metallic membrane (using the PFMM model). Our predictions show that, for a nanocrystalline sample with small grain size (4 nm), a variation in residual stress of 20 MPa (typical in today's microfabrication techniques) would result in a variation on the critical resolved shear yield stress of approximately 15 MPa, a very small fraction of the nominal value of approximately 9 GPa.

The article describes recent progress in atomic and molecular level modeling and simulation of nanoscale materials and processes, as well as efforts by the US National Science Foundation's Network for Computational Nanotechnology (NCN) to cyber-enable such simulation tools together with instructional materials and research seminars. We believe that making advanced simulation tools widely and easily available to the research and education community will significantly enhance the impact of modeling and simulation on nanoscience and nanotechnology To materialize this vision, NCN established nanoHUB.org, a next-generation Web portal or science gateway that lets users run live, interactive simulations, explore data, and learn-all though a simple Web browser without installing any software or providing compute cycles.

We use molecular dynamics with a first-principles-based force field and density functional theory to predict the atomic structure, energetics, and elastic properties of Si nanotubes. We find various low-energy and low-symmetry hollow structures with external diameters of about 1 nm. These are the most stable structures in this small-diameter regime reported so far and exhibit properties very different from the bulk. While the cohesive energies of the four most stable nanotubes reported here are similar from 0.638 to 0.697 eV above bulk Si , they have disparate Young’s moduli from 72 to 123 GPa .

We used first principles methods to study static and dynamical mechanical properties of the ferroelectric polymer poly(vinylidene fluoride) (PVDF) and its copolymer with trifluoro ethylene (TrFE). We use density functional theory [within the generalized gradient approximation (DFT-GGA)] to calculate structure and energetics for various crystalline phases for PVDF and P(VDF-TrFE). We find that the lowest energy phase for PVDF is a nonpolar crystal with a combination of trans (T) and gauche (G) bonds; in the case of the copolymer the role of the extra (bulkier) F atoms is to stabilize T bonds. This leads to the higher crystallinity and piezoelectricity observed experimentally. Using the MSXX first principles-based force field (FF) with molecular dynamics (MD), we find that the energy barrier necessary to nucleate a kink (gauche pairs separated by trans bonds) in an all-T crystal is much lower (14.9 kcal/mol) in P(VDF-TrFE) copolymer than in PVDF (24.8 kcal/mol). This correlates with the observation that the polar phase of the copolymer exhibits a solid-solid transition to a nonpolar phase under heating while PVDF directly melts. We also studied the mobility of an interface between polar and nonpolar phases under uniaxial stress; we find a lower threshold stress and a higher mobility in the copolymer as compared with PVDF. Finally, considering plastic deformation under applied shear, we find that the chains for P(VDF-TrFE) have a very low resistance to sliding, particularly along the chain direction. The atomistic characterization of these "unit mechanisms" provides essential input to mesoscopic or macroscopic models of electro-active polymers.

We develop a many-body force field (FF) for tantalum based on extensive ab initio quantum mechanical (QM) calculations and illustrate its application with molecular dynamics (MD). As input data to the FF we use ab initio methods (LAPW-GGA) to calculate: (i) the zero temperature equation of state (EOS) of Ta for bcc, fcc, and hcp crystal structures for pressures up to ∼500 GPa, and (ii) elastic constants. We use a mixed-basis pseudopotential code to calculate: (iii) volume-relaxed vacancy formation energy also as a function of pressure. In developing the Ta FF we also use previous QM calculations of: (iv) the EOS for the A15 structure; (v) the surface energy bcc (100); (vi) energetics for shear twinning of the bcc crystal. We find that, with appropriate parameters, an embedded atom model FF (denoted as qEAM FF) is able to reproduce all this QM data. We illustrate the use of the qEAM FF with MD to calculate such finite temperature properties as the melting curve up to 300 GPa and thermal expansivity in a wide temperature range. Both our predictions agree well with experimental values.

We explore the apparent discrepancy between experimental data and theoretical calculations of the lattice resistance of bcc tantalum. We present an empirical potential calculation for the temperature dependence of the Peierls stress in this system and an ab initio calculation of the zero-temperature Peierls stress, which employs periodic boundary conditions, those best suited to the study of metallic systems at the electronic-structure level. Our ab initio value for the Peierls stress is over five times larger than current extrapolations of experimental lattice resistance to zero temperature. Although we find that the common techniques for such extrapolation indeed tend to underestimate the zero-temperature limit, the amount of the underestimation we observe is only 10%-20%, leaving open the possibility that mechanisms other than the lattice resistance to motion of an isolated, straight dislocation are important in controlling the process of low-temperature slip.

In this paper we present a modeling approach to bridge the atomistic with macroscopic scales in crystalline materials. The methodology combines identification and modeling of the controlling unit processes at microscopic level with the direct atomistic determination of fundamental material properties. These properties are computed using a many body Force Field derived from ab initio quantum-mechanical calculations. This approach is exercised to describe the mechanical response of high-purity Tantalum single crystals, including the effect of temperature and strain-rate on the hardening rate. The resulting atomistically informed model is found to capture salient features of the behavior of these crystals such as: the dependence of the initial yield point on temperature and strain rate; the presence of a marked stage I of easy glide, specially at low temperatures and high strain rates; the sharp onset of stage II hardening and its tendency to shift towards lower strains, and eventually disappear, as the temperature increases or the strain rate decreases; the parabolic stage II hardening at low strain rates or high temperatures; the stage II softening at high strain rates or low temperatures; the trend towards saturation at high strains; the temperature and strain-rate dependence of the saturation stress; and the orientation dependence of the hardening rate.

The Peierls stress for a [111]-screw dislocation in bcc Tantalum is calculated using an embedded atom potential. More importantly, a method is presented which allows accurate calculations of the Peierls stress in the smallest periodic cells. This method can be easily applied to ab initio calculations, where only the smallest unit cells capable of containing a dislocation can be conviently used. The calculation specifically focuses on the case where the maximum resolved shear stress is along a {110}-plane.

We study fragmentation of small atomistic clusters via molecular dynamics. We calculate the time scales related to fragment formation and emission. We also show that some degree of thermalization is achieved during the expansion process, which allows the determination of a local temperature. In this way we can calculate the break-up temperature as a function of excitation energy, i.e. the fragmentation caloric curve. Fragmentation appears as a rather constant temperature region of the caloric curve. Furthermore, we show that different definitions of temperature, related to different degrees of freedom, yield very similar values.