Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

2 June 2021

Papers citing "Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting"

8 / 8 papers shown

Title
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics Paul Fuchs Stephan Thaler Sebastien Röcken J. Zavadlav DiffM 70 6 0 28 Aug 2024
Predicting solvation free energies with an implicit solvent machine learning potential Sebastien Röcken A. F. Burnet J. Zavadlav AI4Cl AI4CE 66 3 0 31 May 2024
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls Stephan Thaler Gregor Doehner J. Zavadlav 27 21 0 15 Dec 2022
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics Maciej Majewski Adriana Pérez Philipp Thölke Stefan Doerr N. Charron T. Giorgino B. Husic C. Clementi Frank Noé Gianni de Fabritiis AI4CE 14 70 0 14 Dec 2022
Learning Pair Potentials using Differentiable Simulations Wujie Wang Zhenghao Wu Rafael Gómez-Bombarelli 17 23 0 16 Sep 2022
Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics Pantelis R. Vlachas J. Zavadlav M. Praprotnik P. Koumoutsakos AI4CE 23 3 0 17 Feb 2021
Coarse Graining Molecular Dynamics with Graph Neural Networks B. Husic N. Charron Dominik Lemm Jiang Wang Adria Pérez ... Yaoyi Chen Simon Olsson Gianni de Fabritiis Frank Noé C. Clementi AI4CE 35 158 0 22 Jul 2020
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 100 49 0 27 Feb 2020