Jörg Behler
Jörg Behler
Verified email at - Homepage
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
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
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
J Behler
Physical Chemistry Chemical Physics 13 (40), 17930-17955, 2011
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
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
Machine learning molecular dynamics for the simulation of infrared spectra
M Gastegger, J Behler, P Marquetand
Chemical science 8 (10), 6924-6935, 2017
Four generations of high-dimensional neural network potentials
J Behler
Chemical Reviews 121 (16), 10037-10072, 2021
Representing potential energy surfaces by high-dimensional neural network potentials
J Behler
Journal of Physics: Condensed Matter 26 (18), 183001, 2014
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
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
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
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
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
High-dimensional neural network potentials for metal surfaces: A prototype study for copper
N Artrith, J Behler
Physical Review B 85 (4), 045439, 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
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
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
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
The system can't perform the operation now. Try again later.
Articles 1–20