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Jörg Behler

Showing results (1-10 of 89) with videos related to

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Physical Chemistry Chemical Physics : PCCP|September 15, 2011
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulationsJörg Behler
Angewandte Chemie (International Ed. in English)|May 19, 2017
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed SystemsJörg Behler
The Journal of Chemical Physics|November 10, 2016
Perspective: Machine learning potentials for atomistic simulationsJörg Behler
Chemical Reviews|March 29, 2021
Four Generations of High-Dimensional Neural Network PotentialsJörg Behler
The Journal of Chemical Physics|August 12, 2017
Erratum: "Perspective: Machine learning potentials for atomistic simulations" [J. Chem. Phys. 145, 170901 (2016)]Jörg Behler
The Journal of Chemical Physics|February 24, 2011
Atom-centered symmetry functions for constructing high-dimensional neural network potentialsJörg Behler
The Journal of Chemical Physics|January 1, 2022
Insights into lithium manganese oxide-water interfaces using machine learning potentialsMarco Eckhoff, Jörg Behler
Physical Review Letters|May 16, 2007
Generalized neural-network representation of high-dimensional potential-energy surfacesJörg Behler, Michele Parrinello
Physical 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 Behler
The Journal of Physical Chemistry. A|April 6, 2013
A density-functional theory-based neural network potential for water clusters including van der Waals correctionsTobias Morawietz, Jörg Behler
Pageof 9

Showing results (1-10 of 89) with videos related to

Sort By:
Pageof 9
Physical Chemistry Chemical Physics : PCCP|September 15, 2011
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulationsJörg Behler
Angewandte Chemie (International Ed. in English)|May 19, 2017
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed SystemsJörg Behler
The Journal of Chemical Physics|November 10, 2016
Perspective: Machine learning potentials for atomistic simulationsJörg Behler
Chemical Reviews|March 29, 2021
Four Generations of High-Dimensional Neural Network PotentialsJörg Behler
The Journal of Chemical Physics|August 12, 2017
Erratum: "Perspective: Machine learning potentials for atomistic simulations" [J. Chem. Phys. 145, 170901 (2016)]Jörg Behler
The Journal of Chemical Physics|February 24, 2011
Atom-centered symmetry functions for constructing high-dimensional neural network potentialsJörg Behler
The Journal of Chemical Physics|January 1, 2022
Insights into lithium manganese oxide-water interfaces using machine learning potentialsMarco Eckhoff, Jörg Behler
Physical Review Letters|May 16, 2007
Generalized neural-network representation of high-dimensional potential-energy surfacesJörg Behler, Michele Parrinello
Physical 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 Behler
The Journal of Physical Chemistry. A|April 6, 2013
A density-functional theory-based neural network potential for water clusters including van der Waals correctionsTobias Morawietz, Jörg Behler
Pageof 9