Issue 8, 2025

Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023

Abstract

We present the second part of the rigorous evaluation of modern machine learning force fields (MLFFs) within the TEA Challenge 2023. This study provides an in-depth analysis of the performance of MACE, SO3krates, sGDML, SOAP/GAP, and FCHL19* in modeling molecules, molecule-surface interfaces, and periodic materials. We compare observables obtained from molecular dynamics (MD) simulations using different MLFFs under identical conditions. Where applicable, density-functional theory (DFT) or experiment serves as a reference to reliably assess the performance of the ML models. In the absence of DFT benchmarks, we conduct a comparative analysis based on results from various MLFF architectures. Our findings indicate that, at the current stage of MLFF development, the choice of ML model is in the hands of the practitioner. When a problem falls within the scope of a given MLFF architecture, the resulting simulations exhibit weak dependency on the specific architecture used. Instead, emphasis should be placed on developing complete, reliable, and representative training datasets. Nonetheless, long-range noncovalent interactions remain challenging for all MLFF models, necessitating special caution in simulations of physical systems where such interactions are prominent, such as molecule-surface interfaces. The findings presented here reflect the state of MLFF models as of October 2023.

Graphical abstract: Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023

Supplementary files

Article information

Article type
Edge Article
Submitted
26 Sep 2024
Accepted
25 Dec 2024
First published
03 Feb 2025
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2025,16, 3738-3754

Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023

I. Poltavsky, M. Puleva, A. Charkin-Gorbulin, G. Fonseca, I. Batatia, N. J. Browning, S. Chmiela, M. Cui, J. T. Frank, S. Heinen, B. Huang, S. Käser, A. Kabylda, D. Khan, C. Müller, A. J. A. Price, K. Riedmiller, K. Töpfer, T. W. Ko, M. Meuwly, M. Rupp, G. Csányi, O. Anatole von Lilienfeld, J. T. Margraf, K. Müller and A. Tkatchenko, Chem. Sci., 2025, 16, 3738 DOI: 10.1039/D4SC06530A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements