Strachan Group

Large language models (LLMs) are changing the way researchers interact with code and data in scientific computing. While their ability to generate general-purpose code is well established, their effectiveness in producing scientifically valid scripts for domain-specific language (DSLs) remains largely unexplored. We propose an evaluation procedure that enables domain experts to assess the validity of LLM-generated input files for LAMMPS, a widely used molecular dynamics (MD) code, without requiring deep familiarity with its syntax. The evaluation procedure combines a normalization step that produces canonical input files with an extensible parser for syntax analysis, followed by a reduced-cost execution stage and accuracy checks that isolate common errors before running costly simulations. We apply the pipeline to eight state-of-the-art LLMs across three prompts of increasing complexity. The parser pass rate has improved from 74% to 91% over the past year, but scientific accuracy on coupled multi-step workflows remains limited. Across all 80 scripts evaluated on the most complex prompt, only one was fully correct as generated. We further package the automated stages as a reusable agentic skill that LLMs can invoke during script generation; in a small-scale demonstration, this skill helped two models produce five fully correct scripts out of six across the same three prompts, including the hardest one. The pipeline highlights both the limitations of current LLMs in generating scientific DSLs and a practical path toward integrating them into domain-specific computational ecosystems.

Melt processing of molecular crystals has several advantages over alternative routes for manufacturing materials such as pharmaceuticals, organic photovoltaics, and energetic materials. The experimental characterization of the materials properties required to assess melt processability (melting temperature, boiling temperature, decomposition temperature and vapor pressure) for the 1.3 million known molecular crystals is unfeasible; in fact, our survey of the research literature and open databases resulted in only 43 molecular materials with experimentally measured properties that satisfy a common criterion for melt-casting. We developed multi-task, graph-based neural network models that simultaneously predict these properties using a molecular graph as the only input. Screening databases of known molecules with our ML model resulted 2532 melt-castable candidates, with melting temperature between 343 K and 393 K, boiling and decomposition temperature greater than 453 K, and vapor pressure less than 0.0005 mmHg. Going beyond the space of known molecules, we apply our model with a generative approach to the CHNO chemical space, we discover 55 745 additional novel candidates with promising melt-castable characteristics. This three-orders-of-magnitude expansion highlights the power of coupling ML screening and generative design to accelerate materials discovery.

Refractory Complex Concentrated Alloys (RCCAs) can exhibit exceptional high-temperature strength, making such alloys promising candidates for high-temperature structural applications. However, current RCCAs do not possess the high-temperature oxidation resistance required to survive in oxidizing environments for more than a few hours at or above 1000°C, without relying primarily on an environmental barrier coating. Here, we present a machine-learning framework designed to predict the oxidation-induced specific mass changes of RCCAs exposed for 24 h at 1000°C in air, in order to support the search for oxidation-resistant alloys over a wide range of compositions. A database was constructed of experimental specific mass change data, upon oxidation at 900-1000°C for 24 h in air, for 77 compositions comprised of simple elements, binary alloys, and higher-order elemental systems. We then developed a Gaussian Process Regression (GPR) model with physics-informed descriptors based on oxidation products, capturing the fundamental chemistry of oxide formation and stability. Application of this GPR model to the database yielded a MAE (mean absolute error) test score of 5.78 mg/cm², which was a significant improvement in accuracy relative to models only utilizing traditional alloy-based descriptors. Our model was used to screen over 5,100 quaternary RCCAs, revealing compositions with significantly lower predicted specific mass changes compared to existing literature sources. Overall, this work establishes a versatile and efficient strategy to accelerate the discovery of next-generation RCCAs with enhanced resistance to extreme environments.

The ambiguity surrounding key optical and electronic material properties in wide band gap semiconductors presents a significant obstacle to further advancing the state of the art in Photoconductive Semiconductor Switches (PCSS) development and integration. Most significantly, the ionization potentials of optically active crystal defects remain uncertain across a range of reported measurement results, with indications of a probable difference between the thermal and optical ionization potentials. The factors must be addressed to ensure purposeful design and fabrication of devices operating on the principle of the interactions between these doped defects, the optical excitation, and the host semiconductor substrate. This review compiles the current body of knowledge on optically addressable defects in wide band gap semiconductors and known implementations into prototype devices. Viable pathways toward resolving these unknown design spaces toward future breakthroughs are identified.