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

20 January 2022

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

4 / 4 papers shown

Title
FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials Thomas Plé Olivier Adjoua Louis Lagardère Jean‐Philip Piquemal 46 8 0 02 May 2024
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials Peter K. Eastman Raimondas Galvelis Raúl P. Peláez C. Abreu Stephen E. Farr ... Yuanqing Wang Ivy Zhang J. Chodera Gianni De Fabritiis T. Markland AI4CE VLM 46 37 0 04 Oct 2023
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 177 248 0 01 May 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 254 1,241 0 08 Jan 2021