Jörg Behler

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michele parrinelloDepartment of ChemistryVerified email at phys.chem.ethz.ch
Marco BernasconiUniversity of Milano-BicoccaVerified email at unimib.it
Karsten ReuterDirector at Fritz-Haber-Institut der MPGVerified email at fhi-berlin.mpg.de
Gabriele C. SossoProfessor | Department of Chemistry - University of WarwickVerified email at warwick.ac.uk
Matthias SchefflerDirector of NOMAD Laboratory at FHI of Max-Planck-GesellschaftVerified email at fhi-berlin.mpg.de
Christoph DellagoProfessor of Computational Physics, University of ViennaVerified email at univie.ac.at
Tobias MorawietzResearch ScientistVerified email at bayer.com
Nongnuch ArtrithDebye Institute for Nanomaterials Science, Utrecht UniversityVerified email at atomistic.net
Michele CeriottiAssociate Professor at EPFL, Institute of MaterialsVerified email at epfl.ch
Stefan GoedeckerUniversity of Basel, SwitzerlandVerified email at unibas.ch
Tsz Wai KoPostdoc, NanoEngineering, University of California, San DiegoVerified email at ucsd.edu
Davide DonadioUniversity of California DavisVerified email at ucdavis.edu
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeVerified email at cam.ac.uk
Jonas A FinklerAalborg UniversityVerified email at bio.aau.dk
Thomas D. KühneFounding Director of the Center for Advanced Systems Understanding (CASUS), HZDR & TU DresdenVerified email at cp2k.org
Rustam Zakaria KhaliullinMcGill UniversityVerified email at khaliullin.com
Giacomo MiceliEcole Polytechnique Fédérale de Lausanne (EPFL)Verified email at epfl.ch
Marco EckhoffMicrosoft, QuantumVerified email at microsoft.com
André FielickeFritz-Haber-Institut der Max-Planck-GesellschaftVerified email at fhi-berlin.mpg.de
G.J. KroesProfessor of Theoretical Chemistry, Leiden UniversityVerified email at chem.leidenuniv.nl

Jörg Behler

Ruhr-Universität Bochum

Verified email at ruhr-uni-bochum.de - Homepage

neural network potentials materials science


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Generalized neural-network representation of high-dimensional potential-energy surfaces J Behler, M Parrinello Physical review letters 98 (14), 146401, 2007	4671	2007
Atom-centered symmetry functions for constructing high-dimensional neural network potentials J Behler The Journal of chemical physics 134 (7), 074106, 2011	1726	2011
Perspective: Machine learning potentials for atomistic simulations J Behler The Journal of Chemical Physics 145 (17), 170901, 2016	1483	2016
Constructing high‐dimensional neural network potentials: A tutorial review J Behler International Journal of Quantum Chemistry 115 (16), 1032-1050, 2015	899	2015
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations J Behler Physical Chemistry Chemical Physics 13 (40), 17930-17955, 2011	820	2011
Performance and Cost Assessment of Machine Learning Interatomic Potentials Y Zuo, C Chen, X Li, Z Deng, Y Chen, J Behler, G Csányi, AV Shapeev, ... The Journal of Physical Chemistry A 124 (4), 731-745, 2020	768	2020
First principles neural network potentials for reactive simulations of large molecular and condensed systems J Behler Angewandte Chemie International Edition 56 (42), 12828-12840, 2017	688	2017
Four generations of high-dimensional neural network potentials J Behler Chemical Reviews 121 (16), 10037-10072, 2021	635	2021
Machine learning molecular dynamics for the simulation of infrared spectra M Gastegger, J Behler, P Marquetand Chemical science 8 (10), 6924-6935, 2017	542	2017
How van der Waals interactions determine the unique properties of water T Morawietz, A Singraber, C Dellago, J Behler Proceedings of the National Academy of Sciences 113 (30), 8368-8373, 2016	464	2016
Representing potential energy surfaces by high-dimensional neural network potentials J Behler Journal of Physics: Condensed Matter 26 (18), 183001, 2014	454	2014
A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer TW Ko, JA Finkler, S Goedecker, J Behler Nature Communications 12 (1), 1-11, 2021	446	2021
High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide N Artrith, T Morawietz, J Behler Physical Review B 83 (15), 153101, 2011	441	2011
Nucleation mechanism for the direct graphite-to-diamond phase transition RZ Khaliullin, H Eshet, TD Kühne, J Behler, M Parrinello Nature Materials 10 (9), 693-697, 2011	382	2011
High-dimensional neural network potentials for metal surfaces: A prototype study for copper N Artrith, J Behler Physical Review B 85 (4), 045439, 2012	354	2012
Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials G Imbalzano, A Anelli, D Giofré, S Klees, J Behler, M Ceriotti The Journal of Chemical Physics 148 (24), 241730, 2018	350	2018
Dissociation of O 2 at Al (111): The role of spin selection rules J Behler, B Delley, S Lorenz, K Reuter, M Scheffler Physical review letters 94 (3), 036104, 2005	350	2005
Ab initio thermodynamics of liquid and solid water B Cheng, EA Engel, J Behler, C Dellago, M Ceriotti Proceedings of the National Academy of Sciences 116 (4), 1110-1115, 2019	324	2019
Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials A Singraber, J Behler, C Dellago Journal of chemical theory and computation 15 (3), 1827-1840, 2019	323	2019
Neural network interatomic potential for the phase change material GeTe GC Sosso, G Miceli, S Caravati, J Behler, M Bernasconi Physical Review B 85 (17), 174103, 2012	284	2012

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