TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular
  Simulations
Raul P. Pelaez Guillem Simeon Raimondas Galvelis Antonio Mirarchi Peter Eastman Stefan Doerr Philipp Thölke Thomas E. Markland Gianni De Fabritiis
Achieving a balance between computational speed, prediction accuracy, and
universal applicability in molecular simulations has been a persistent
challenge. This paper presents substantial advancements in the TorchMD-Net
software, a pivotal step forward in the shift from conventional force fields to
neural network-based potentials. The evolution of TorchMD-Net into a more
comprehensive and versatile framework is highlighted, incorporating
cutting-edge architectures such as TensorNet. This transformation is achieved
through a modular design approach, encouraging customized applications within
the scientific community. The most notable enhancement is a significant
improvement in computational efficiency, achieving a very remarkable
acceleration in the computation of energy and forces for TensorNet models, with
performance gains ranging from 2-fold to 10-fold over previous iterations.
Other enhancements include highly optimized neighbor search algorithms that
support periodic boundary conditions and the smooth integration with existing
molecular dynamics frameworks. Additionally, the updated version introduces the
capability to integrate physical priors, further enriching its application
spectrum and utility in research. The software is available at
https://github.com/torchmd/torchmd-net.