Jörg Behler
Cited by
Cited by
Generalized neural-network representation of high-dimensional potential-energy surfaces
J Behler, M Parrinello
Physical review letters 98 (14), 146401, 2007
Atom-centered symmetry functions for constructing high-dimensional neural network potentials
J Behler
The Journal of chemical physics 134 (7), 074106, 2011
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
J Behler
Physical Chemistry Chemical Physics 13 (40), 17930-17955, 2011
Perspective: Machine learning potentials for atomistic simulations
J Behler
The Journal of Chemical Physics 145 (17), 170901, 2016
Constructing high‐dimensional neural network potentials: A tutorial review
J Behler
International Journal of Quantum Chemistry 115 (16), 1032-1050, 2015
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
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
Representing potential energy surfaces by high-dimensional neural network potentials
J Behler
Journal of Physics: Condensed Matter 26 (18), 183001, 2014
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
Metadynamics simulations of the high-pressure phases of silicon employing a high-dimensional neural network potential
J Behler, R Martoňák, D Donadio, M Parrinello
Physical review letters 100 (18), 185501, 2008
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
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
Machine learning molecular dynamics for the simulation of infrared spectra
M Gastegger, J Behler, P Marquetand
Chemical science 8 (10), 6924-6935, 2017
Structure determination of isolated metal clusters via far-infrared spectroscopy
A Fielicke, A Kirilyuk, C Ratsch, J Behler, M Scheffler, G von Helden, ...
Physical review letters 93 (2), 023401, 2004
High-dimensional neural network potentials for metal surfaces: A prototype study for copper
N Artrith, J Behler
Physical Review B 85 (4), 045439, 2012
Nonadiabatic effects in the dissociation of oxygen molecules at the Al (111) surface
J Behler, K Reuter, M Scheffler
Physical Review B 77 (11), 115421, 2008
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
Representing molecule-surface interactions with symmetry-adapted neural networks
J Behler, S Lorenz, K Reuter
The Journal of chemical physics 127 (1), 07B603, 2007
Ab initio quality neural-network potential for sodium
H Eshet, RZ Khaliullin, TD Kühne, J Behler, M Parrinello
Physical Review B 81 (18), 184107, 2010
Fast Crystallization of the Phase Change Compound GeTe by Large-Scale Molecular Dynamics Simulations
GC Sosso, G Miceli, S Caravati, F Giberti, J Behler, M Bernasconi
The journal of physical chemistry letters 4 (24), 4241-4246, 2013
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