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

Igor Poltavsky¹, Mirela Puleva^1,2, Anton Charkin-Gorbulin^1,3

¹Department of Physics and Materials Science, University of Luxembourg L-1511 Luxembourg Luxembourg alexandre.tkatchenko@uni.lu igor.poltavskyi@uni.lu.

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Summary

Modern machine learning force fields (MLFFs) show similar performance across architectures for many molecular modeling tasks. Focus on high-quality training data, as long-range interactions remain a challenge for all MLFFs.

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Area of Science:

Computational Chemistry
Materials Science
Machine Learning

Background:

Machine learning force fields (MLFFs) are increasingly used in molecular simulations.
Rigorous evaluation of MLFF performance is crucial for reliable scientific discovery.
The TEA Challenge 2023 provides a platform for benchmarking MLFFs.

Purpose of the Study:

To evaluate the performance of leading MLFFs (MACE, SO3krates, sGDML, SOAP/GAP, FCHL19*) in modeling molecules, interfaces, and materials.
To compare simulation results from different MLFFs under identical conditions.
To assess the impact of MLFF architecture versus training data quality on simulation accuracy.

Main Methods:

Conducting molecular dynamics (MD) simulations using various MLFFs.
Comparing MLFF results against density-functional theory (DFT) or experimental data where available.
Performing comparative analysis across different MLFF architectures in the absence of DFT benchmarks.

Main Results:

MLFF performance is largely dependent on the quality and representativeness of training datasets rather than specific architectures.
Simulations show weak dependency on MLFF architecture when the problem is within the model's scope.
Long-range noncovalent interactions pose a significant challenge for all current MLFFs.

Conclusions:

Practitioners can choose MLFFs based on their specific needs, but dataset quality is paramount.
Careful consideration is needed for systems dominated by long-range noncovalent interactions, like molecule-surface interfaces.
The findings reflect the state of MLFF development as of October 2023.