GPT-4 as an interface between researchers and computational software: improving usability and reproducibility. (arXiv:2310.11458v1 [cond-mat.mtrl-sci])

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.



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