On the Inclusion of Charge and Spin States in Cartesian Tensor Neural Network Potentials

no code implementations • 22 Mar 2024 • Guillem Simeon, Antonio Mirarchi, Raul P. Pelaez, Raimondas Galvelis, Gianni de Fabritiis

In this letter, we present an extension to TensorNet, a state-of-the-art equivariant Cartesian tensor neural network potential, allowing it to handle charged molecules and spin states without architectural changes or increased costs.

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

1 code implementation • 27 Feb 2024 • 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.

Computational Efficiency