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Physical Review Letters|May 16, 2007
Generalized neural-network representation of high-dimensional potential-energy surfacesJörg Behler, Michele ParrinelloPhysical Review Letters|June 4, 2008
Metadynamics simulations of the high-pressure phases of silicon employing a high-dimensional neural network potentialJörg Behler, Roman Martonák, Davide Donadio, et al.Physical Review Letters|May 1, 2012
Microscopic origins of the anomalous melting behavior of sodium under high pressureHagai Eshet, Rustam Z Khaliullin, Thomas D Kühne, et al.Nature Materials|July 26, 2011
Nucleation mechanism for the direct graphite-to-diamond phase transitionRustam Z Khaliullin, Hagai Eshet, Thomas D Kühne, et al.Physical 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 BehlerPageof 34