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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

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"

9 / 9 papers shown
Title
A Langevin sampling algorithm inspired by the Adam optimizer
A Langevin sampling algorithm inspired by the Adam optimizer
B. Leimkuhler
René Lohmann
P. Whalley
79
0
0
26 Apr 2025
Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations
Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations
Henrik Christiansen
Takashi Maruyama
Federico Errica
Viktor Zaverkin
M. Takamoto
Francesco Alesiani
78
0
0
26 Mar 2025
Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty
  Design -- A Perspective
Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective
Yuzhi Xu
Haowei Ni
Qinhui Gao
Chia-Hua Chang
Yanran Huo
...
Yike Zhang
Radu Grovu
Min He
John Z. H. Zhang
Yuanqing Wang
AI4CE
29
0
0
08 Oct 2024
On the design space between molecular mechanics and machine learning
  force fields
On the design space between molecular mechanics and machine learning force fields
Yuanqing Wang
Kenichiro Takaba
Michael S. Chen
Marcus Wieder
Yuzhi Xu
...
Kyunghyun Cho
Joe G. Greener
Peter K. Eastman
Stefano Martiniani
M. Tuckerman
AI4CE
42
4
0
03 Sep 2024
GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and
  Portability
GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability
Andrey Alekseenko
Szilárd Páll
Erik Lindahl
24
2
0
02 May 2024
Fusing Neural and Physical: Augment Protein Conformation Sampling with
  Tractable Simulations
Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
Jiarui Lu
Zuobai Zhang
Bozitao Zhong
Chence Shi
Jian Tang
AI4CE
38
1
0
16 Feb 2024
SPICE, A Dataset of Drug-like Molecules and Peptides for Training
  Machine Learning Potentials
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
37
105
0
21 Sep 2022
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and
  Nonlocal Effects
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
174
246
0
01 May 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate
  Interatomic Potentials
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
206
1,240
0
08 Jan 2021
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