Jörg Behler

Full Professor

Germany

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About

Jörg Behler is a Full Professor at the Research Alliance Ruhr in Germany. His research focuses on the application of machine learning techniques to atomistic simulations, particularly related to oxide–water interfaces and electronic structure calculations. He has contributed significantly to understanding chemical reactions in solution and the development of neural network potentials.

Articles (20)

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2021

Collaborators (5)

Andreas Heyden

Professor

University of South Carolina

UNITED STATES

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Zongrui Pei

New York University

UNITED STATES

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Stefan Goedecker

University of Basel

SWITZERLAND

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Moritz Gubler

University of Basel

SWITZERLAND

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Toru Wada

Assistant professor

Japan Advanced Institute of Science and Technology

JAPAN

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Jörg Behler

Research Interests

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About

Articles (20)

Accuracy of charge densities in electronic structure calculations

Roadmap for the development of machine learning-based interatomic potentials

Atomistic simulations of oxide–water interfaces using machine learning potentials

Impact of the damping function in dispersion-corrected density functional theory on the properties of liquid water

Free energy profiles for chemical reactions in solution from high-dimensional neural network potentials: The case of the Strecker synthesis

Iterative charge equilibration for fourth-generation high-dimensional neural network potentials

A high-dimensional neural network potential for Co3O4

Random Sampling Versus Active Learning Algorithms for Machine Learning Potentials of Quantum Liquid Water

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces

Nuclear Quantum Effects in Liquid Water Are Marginal for Its Average Structure but Significant for Dynamics

Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding

Accelerating Non-Empirical Structure Determination of Ziegler–Natta Catalysts with a High-Dimensional Neural Network Potential

Machine learning transferable atomic forces for large systems from underconverged molecular fragments

Coupled Cluster Molecular Dynamics of Condensed Phase Systems Enabled by Machine Learning Potentials: Liquid Water Benchmark

A Hessian-based assessment of atomic forces for training machine learning interatomic potentials

High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark

Insights into lithium manganese oxide–water interfaces using machine learning potentials

High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions

A bin and hash method for analyzing reference data and descriptors in machine learning potentials

Four Generations of High-Dimensional Neural Network Potentials

Collaborators (5)

Andreas Heyden

Zongrui Pei

Stefan Goedecker

Moritz Gubler

Toru Wada

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