Ethan Holbrook

Ethan Holbrook, Juan C. Verduzco, Alejandro Strachan

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 code/input scripting for domain-specific languages (DSLs) remains largely unexplored. We propose an evaluation procedure that enables domain experts (who may not be experts in the DSL) to assess the validity of LLM-generated input files for LAMMPS, a widely used molecular dynamics (MD) code, and to use those assessments to evaluate the performance of state-of-the-art LLMs and identify common issues. Key to the evaluation procedure are a normalization step to generate canonical files and an extensible parser for syntax analysis. The following steps isolate common errors without incurring costly tests (in time and computational resources). Once a working input file is generated, LLMs can accelerate verification tests. Our findings highlight limitations of LLMs in generating scientific DSLs and a practical path forward for their integration into domain-specific computational ecosystems by domain experts.

Juan C. Verduzco, Ethan Holbrook, Alejandro Strachan

Large language models (LLMs) are playing an increasingly important role in science and engineering. For example, their ability to parse and understand human and computer languages makes them powerful interpreters and their use in applications like code generation are well-documented. We explore the ability of the GPT-4 LLM to ameliorate two major challenges in computational materials science: i) the high barriers for adoption of scientific software associated with the use of custom input languages, and ii) the poor reproducibility of published results due to insufficient details in the description of simulation methods. We focus on a widely used software for molecular dynamics simulations, the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), and quantify the usefulness of input files generated by GPT-4 from task descriptions in English and its ability to generate detailed descriptions of computational tasks from input files. We find that GPT-4 can generate correct and ready-to-use input files for relatively simple tasks and useful starting points for more complex, multi-step simulations. In addition, GPT-4's description of computational tasks from input files can be tuned from a detailed set of step-by-step instructions to a summary description appropriate for publications. Our results show that GPT-4 can reduce the number of routine tasks performed by researchers, accelerate the training of new users, and enhance reproducibility.