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

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

20 January 2022

Raimondas Galvelis

Alejandro Varela-Rial

Peter K. Eastman

Gianni De Fabritiis

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

8 / 8 papers shown

Title
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields Ilyes Batatia D. P. Kovács G. Simm Christoph Ortner Gábor Csányi 87 496 0 15 Jun 2022
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials Philipp Thölke Gianni De Fabritiis AI4CE 91 196 0 05 Feb 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 191 255 0 01 May 2021
Equivariant message passing for the prediction of tensorial properties and molecular spectra Kristof T. Schütt Oliver T. Unke M. Gastegger 110 541 0 05 Feb 2021
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules Johannes Klicpera Shankari Giri Johannes T. Margraf Stephan Günnemann 79 324 0 28 Nov 2020
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features Zhuoran Qiao Matthew Welborn Anima Anandkumar F. Manby Thomas F. Miller AI4CE 70 217 0 15 Jul 2020
Machine learning for molecular simulation Frank Noé A. Tkatchenko K. Müller C. Clementi AI4CE 75 664 0 07 Nov 2019
Less is more: sampling chemical space with active learning Justin S. Smith B. Nebgen Nicholas Lubbers Olexandr Isayev A. Roitberg 60 618 0 28 Jan 2018

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