Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices

TL;DR

This paper offers comprehensive guidelines and best practices for documenting, validating, and reporting machine learning interatomic potentials to improve reproducibility and reliability in materials simulations.

Contribution

It provides a detailed checklist and recommendations for standardizing documentation and validation procedures for MLIPs, enhancing transparency and reproducibility.

Findings

Developed a step-by-step reporting checklist for MLIP studies.

Outlined best practices for data generation, model training, and validation.

Promoted standardized reporting to facilitate independent evaluation.

Abstract

Recent developments in machine learning interatomic potentials (MLIPs) have empowered even non-experts in machine learning to train MLIPs for accelerating materials simulations. However, the current literature lacks clear standards for documenting the use of MLIPs, which hinders the reproducibility and independent evaluation of the presented results. In this perspective, we aim to provide guidance on best practices for documenting MLIP use while walking the reader through the development and deployment of MLIPs including hardware and software requirements, generating training data, training models, validating predictions, and MLIP inference. We also suggest useful plotting practices and analyses to validate and boost confidence in the deployed models. Finally, we provide a step-by-step checklist for practitioners to use directly before publication to standardize the information to be reported. Overall, we hope that our work will encourage reliable and reproducible use of these MLIPs, which will accelerate their ability to make a positive impact in various disciplines including materials science, chemistry, and biology, among others.