Stability and transferability of machine learning force fields for molecular dynamics applications†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Salatan Duangdangchote, Dwight S. Seferos and Oleksandr Voznyy
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Abstract

In this study, we focus on simplifying the generation of Machine Learning Force Fields (MLFFs) for Molecular Dynamics (MD) simulations of inorganic materials, with an emphasis on sustainable use of computational resources. We evaluate the efficiency and accuracy of existing state-of-the-art graph neural network (GNN) models and introduce new benchmarks that go beyond conventional mean absolute error on forces and energies. We showcase our methodology on the example of lithium-ion conductor materials, paving the way to a broader screening of ionic conductors for batteries and fuel cells.

Abstract Image

用于分子动力学应用的机器学习力场的稳定性和可转移性†。
在本研究中,我们将重点放在简化用于无机材料分子动力学(MD)模拟的机器学习力场(MLFF)的生成上,强调计算资源的可持续利用。我们评估了现有最先进的图神经网络(GNN)模型的效率和准确性,并引入了新的基准,超越了传统的力和能量平均绝对误差。我们以锂离子导体材料为例展示了我们的方法,为更广泛地筛选电池和燃料电池的离子导体铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.80
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0.00%
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