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

27 February 2024

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

6 / 6 papers shown

Title
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics Antonio Mirarchi Raúl P. Peláez Guillem Simeon Gianni de Fabritiis 13 3 0 26 Sep 2024
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials Peter K. Eastman P. Behara David L. Dotson Raimondas Galvelis John E. Herr ... J. Chodera Benjamin P. Pritchard Yuanqing Wang Gianni de Fabritiis T. Markland 24 105 0 21 Sep 2022
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs Yi-Lun Liao Tess E. Smidt 73 211 0 23 Jun 2022
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects Oliver T. Unke Stefan Chmiela M. Gastegger Kristof T. Schütt H. E. Sauceda K. Müller 142 242 0 01 May 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 166 1,095 0 27 Apr 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 188 1,218 0 08 Jan 2021