Jörg Behler
Affiliations: | Georg-August-Universität Göttingen, Göttingen, Niedersachsen, Germany |
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Parents
Sign in to add mentor| Karsten Reuter | grad student | 2000-2004 | Fritz Haber Institute of the Max Planck Society (Physics Tree) |
| Matthias Scheffler | grad student | 2000-2004 | Fritz Haber Institute (Physics Tree) |
| Michele Parrinello | post-doc | 2005-2007 | ETH Zürich |
| Dominik Marx | post-doc | 2007-2008 | Ruhr-Universität Bochum |
Children
Sign in to add trainee| Nongnuch Artrith | grad student | ||
| Tobias Morawietz | grad student | Ruhr University–Bochum, Bochum, German |
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Publications
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| Abedi M, Behler J, Goldsmith CF. (2023) High-Dimensional Neural Network Potentials for Accurate Prediction of Equation of State: A Case Study of Methane. Journal of Chemical Theory and Computation. 19: 7825-7832 |
| Ko TW, Finkler JA, Goedecker S, et al. (2023) Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding. Journal of Chemical Theory and Computation. 19: 3567-3579 |
| Herbold M, Behler J. (2023) Machine learning transferable atomic forces for large systems from underconverged molecular fragments. Physical Chemistry Chemical Physics : Pccp. 25: 12979-12989 |
| Daru J, Forbert H, Behler J, et al. (2022) Coupled Cluster Molecular Dynamics of Condensed Phase Systems Enabled by Machine Learning Potentials: Liquid Water Benchmark. Physical Review Letters. 129: 226001 |
| Shanavas Rasheeda D, Martín Santa Daría A, Schröder B, et al. (2022) High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark. Physical Chemistry Chemical Physics : Pccp. 24: 29381-29392 |
| Herbold M, Behler J. (2022) A Hessian-based assessment of atomic forces for training machine learning interatomic potentials. The Journal of Chemical Physics. 156: 114106 |
| Kocer E, Ko TW, Behler J. (2022) Neural Network Potentials: A Concise Overview of Methods. Annual Review of Physical Chemistry. 73: 163-186 |
| Eckhoff M, Behler J. (2022) Insights into lithium manganese oxide-water interfaces using machine learning potentials. The Journal of Chemical Physics. 155: 244703 |
| Behler J. (2021) Four Generations of High-Dimensional Neural Network Potentials. Chemical Reviews |
| Ko TW, Finkler JA, Goedecker S, et al. (2021) General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer. Accounts of Chemical Research |