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Physical Chemistry Chemical Physics : PCCP|May 11, 2023
Machine learning transferable atomic forces for large systems from underconverged molecular fragmentsMarius Herbold, Jörg BehlerThe Journal of Chemical Physics|March 23, 2022
A Hessian-based assessment of atomic forces for training machine learning interatomic potentialsMarius Herbold, Jörg BehlerPhysical Chemistry Chemical Physics : PCCP|September 15, 2011
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulationsJörg BehlerAngewandte Chemie (International Ed. in English)|May 19, 2017
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed SystemsJörg BehlerThe Journal of Chemical Physics|November 10, 2016
Perspective: Machine learning potentials for atomistic simulationsJörg BehlerChemical Reviews|March 29, 2021
Four Generations of High-Dimensional Neural Network PotentialsJörg BehlerThe Journal of Chemical Physics|February 24, 2011
Atom-centered symmetry functions for constructing high-dimensional neural network potentialsJörg BehlerThe Journal of Chemical Physics|January 1, 2022
Insights into lithium manganese oxide-water interfaces using machine learning potentialsMarco Eckhoff, Jörg BehlerPhysical Review Letters|May 16, 2007
Generalized neural-network representation of high-dimensional potential-energy surfacesJörg Behler, Michele ParrinelloPhysical Chemistry Chemical Physics : PCCP|October 19, 2017
Surface phase diagram prediction from a minimal number of DFT calculations: redox-active adsorbates on zinc oxideMatti Hellström, Jörg BehlerPageof 9