End-to-End Differentiable Molecular Mechanics Force Field Construction

End-to-End Differentiable Molecular Mechanics Force Field Construction

2 October 2020

Yuanqing Wang

Benjamin Kaminow

Papers citing "End-to-End Differentiable Molecular Mechanics Force Field Construction"

6 / 6 papers shown

Title
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 34 0 0 08 Oct 2024
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 45 4 0 03 Sep 2024
FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials Thomas Plé Olivier Adjoua Louis Lagardère Jean‐Philip Piquemal 37 8 0 02 May 2024
TorchMD: A deep learning framework for molecular simulations Stefan Doerr Maciej Majewski Adria Pérez Andreas Krämer C. Clementi Frank Noe T. Giorgino Gianni De Fabritiis AI4CE 13 170 0 22 Dec 2020
Deep Learning in Protein Structural Modeling and Design Wenhao Gao S. Mahajan Jeremias Sulam Jeffrey J. Gray 29 159 0 16 Jul 2020
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 109 49 0 27 Feb 2020