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

TL;DR

This paper demonstrates that GPT-4 can serve as an effective interface for computational science, generating input files and descriptions to improve usability and reproducibility in molecular dynamics simulations.

Contribution

It introduces a novel application of GPT-4 to generate simulation inputs and descriptions, reducing routine workload and enhancing reproducibility in scientific computing.

Findings

GPT-4 can generate correct input files for simple tasks

GPT-4 provides useful starting points for complex simulations

GPT-4 enhances reproducibility and accelerates user training

Abstract

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.