Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics
Ivan Žugec, Tin Hadži Veljković, Maite Alducin, J. Iñaki Juaristi
TL;DR
Dynamic training improves machine learning models for long-term molecular simulations, offering better accuracy and broader applicability.
Contribution
Dynamic training is introduced as an architecture-independent method to enhance accuracy in long-lasting molecular dynamics simulations.
Findings
Dynamic training applied to an EGNN improved prediction accuracy for a hydrogen-palladium system on graphene.
The method is architecture-independent, making it applicable to various machine learning potentials.
DT offers a practical tool for advancing molecular dynamics simulations with enhanced accuracy.
Abstract
Molecular dynamics (MD) simulations are vital for exploring complex systems in computational physics and chemistry. While machine learning methods dramatically reduce computational costs relative to ab initio methods, their accuracy in long-lasting simulations remains limited. Here we propose dynamic training (DT), a method designed to enhance accuracy of a model over extended MD simulations. Applying DT to an equivariant graph neural network (EGNN) on the challenging system of a hydrogen molecule interacting with a palladium cluster anchored to a graphene vacancy demonstrates a superior prediction accuracy compared to conventional approaches. Crucially, the DT architecture-independent design ensures its applicability across diverse machine learning potentials, making it a practical tool for advancing MD simulations.
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Taxonomy
TopicsMachine Learning in Materials Science · Computational Drug Discovery Methods · Protein Structure and Dynamics