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Large language models (LLMs) are playing an increasingly important role inscience and engineering. For example, their ability to parse and understandhuman and computer languages makes them powerful interpreters and their use inapplications like code generation are well-documented. We explore the abilityof the GPT-4 LLM to ameliorate two major challenges in computational materialsscience: i) the high barriers for adoption of scientific software associatedwith the use of custom input languages, and ii) the poor reproducibility ofpublished results due to insufficient details in the description of simulationmethods. We focus on a widely used software for molecular dynamics simulations,the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), andquantify the usefulness of input files generated by GPT-4 from taskdescriptions in English and its ability to generate detailed descriptions ofcomputational tasks from input files. We find that GPT-4 can generate correctand ready-to-use input files for relatively simple tasks and useful startingpoints for more complex, multi-step simulations. In addition, GPT-4'sdescription of computational tasks from input files can be tuned from adetailed set of step-by-step instructions to a summary description appropriatefor publications. Our results show that GPT-4 can reduce the number of routinetasks performed by researchers, accelerate the training of new users, andenhance reproducibility.

GPT-4 as an interface between researchers and computational software: improving usability and reproducibility