Proceedings of MATSUS Spring 2024 Conference (MATSUS24)
DOI: https://doi.org/10.29363/nanoge.matsus.2024.265
Publication date: 18th December 2023
Machine-learning force fields (MLFF) show high accuracy and efficiency for modeling the potential energy surfaces of molecules, materials, and interfaces. However, the performance of MLFFs greatly depends upon incorporating the physical symmetries. Finding all relevant symmetries becomes a challenging task for large system sizes. Here, we develop a data-driven method based on molecular graphs to reveal relevant permutational symmetries and distinguish atoms with different chemical environments in molecules and materials. The kernel-based model architecture of BIGDML and the message-passing neural network architecture of MACE were enhanced to demonstrate the applicability of the developed method to the most widely used MLFF architectures. The BIGDML model, enhanced with extracted symmetries, demonstrates superior accuracy and performance, enabling comprehensive investigations of complex systems like the 1,8-naphthyridine/graphene interface and its behavior at finite temperatures. MACE was enhanced by expanding atomic species using the extracted distinctive chemical environments, resulting in improved accuracy for CsPbI3 slab systems, particularly notable with larger training sets. Overall, this research underscores the critical role of symmetries in advancing MLFFs for complex systems with broad implications in various research fields.
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