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2310.03121
Cited By
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
4 October 2023
Peter K. Eastman
Raimondas Galvelis
Raúl P. Peláez
C. Abreu
Stephen E. Farr
Emilio Gallicchio
Anton Gorenko
Mike Henry
Frank Hu
Jing Huang
Andreas Krämer
Julien Michel
Joshua A. Mitchell
Vijay S. Pande
J. P. Rodrigues
Jaime Rodríguez-Guerra
Andrew C. Simmonett
Sukrit Singh
J. Swails
Philip Turner
Yuanqing Wang
Ivy Zhang
J. Chodera
Gianni de Fabritiis
T. Markland
AI4CE
VLM
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Papers citing
"OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials"
4 / 4 papers shown
Title
A Langevin sampling algorithm inspired by the Adam optimizer
B. Leimkuhler
René Lohmann
P. Whalley
74
0
0
26 Apr 2025
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
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
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
1