Accurate machine learning force fields via experimental and simulation data fusion

17 August 2023

Papers citing "Accurate machine learning force fields via experimental and simulation data fusion"

6 / 6 papers shown

Title
Enhancing the Efficiency of Complex Systems Crystal Structure Prediction by Active Learning Guided Machine Learning Potential Jiaxiang Li Junwei Feng Jie Luo Bowen Jiang Xiangyu Zheng ... Keith Butler Hanyu Liu Congwei Xie Yu Xie Yanming Ma 21 0 0 13 May 2025
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics Paul Fuchs Stephan Thaler Sebastien Röcken J. Zavadlav DiffM 61 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 63 3 0 31 May 2024
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles Aik Rui Tan S. Urata Samuel Goldman Johannes C. B. Dietschreit Rafael Gómez-Bombarelli BDL 24 41 0 02 May 2023
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 192 1,232 0 08 Jan 2021
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 93 49 0 27 Feb 2020