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

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

no code implementations • 4 Oct 2023 • Peter Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João PGLM Rodrigues, Jaime Rodriguez-Guerra, Andrew C. Simmonett, Sukrit Singh, Jason Swails, Philip Turner, Yuanqing Wang, Ivy Zhang, John D. Chodera, Gianni de Fabritiis, Thomas E. Markland

Machine learning plays an important and growing role in molecular simulation.

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

no code implementations • 21 Sep 2022 • Peter Eastman, Pavan Kumar Behara, David L. Dotson, Raimondas Galvelis, John E. Herr, Josh T. Horton, Yuezhi Mao, John D. Chodera, Benjamin P. Pritchard, Yuanqing Wang, Gianni de Fabritiis, Thomas E. Markland

Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on.

NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic

no code implementations • 20 Jan 2022 • Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter Eastman, Thomas E. Markland, John D. Chodera, Gianni de Fabritiis

Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations.

BIG-bench Machine Learning