TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations

• DOI: 10.1021/acs.jctc.4c00253
• Publication Date: 2024-05-14T14:33:56Z
• Authors: Raul P. Pelaez; Guillem Simeon; Raimondas Galvelis; Antonio Mirarchi; Peter Eastman; Stefan Doerr; Philipp Thölke; Thomas E. Markland; Gianni De Fabritiis
• Keywords: Ranging

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been persistent challenge. This paper presents substantial advancements TorchMD-Net software, pivotal step forward the shift from conventional force fields to neural network-based potentials. The evolution of into more comprehensive versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. transformation achieved through modular design approach, encouraging customized applications within scientific community. most notable enhancement significant improvement efficiency, achieving very remarkable acceleration computation energy forces for TensorNet models, with performance gains ranging 2× 10× over previous, nonoptimized, iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions smooth integration existing dynamics frameworks. Additionally, updated version introduces capability integrate physical priors, further enriching its application spectrum utility research. software available at https://github.com/torchmd/torchmd-net.