Author Behler, Jörg
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2017 | Journal Article
Surface phase diagram prediction from a minimal number of DFT calculations: redox-active adsorbates on zinc oxide
Hellström, M. & Behler, J. (2017)
Physical Chemistry Chemical Physics, 19(42) pp. 28731-28748. DOI: https://doi.org/10.1039/C7CP05182D
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2018 | Journal Article |
Analysis of Energy Dissipation Channels in a Benchmark System of Activated Dissociation: N2 on Ru(0001).
Shakouri, K.; Behler, J.; Meyer, J. & Kroes, G.-J. (2018)
The Journal of Physical Chemistry. C, Nanomaterials and Interfaces, 122(41) pp. 23470-23480. DOI: https://doi.org/10.1021/acs.jpcc.8b06729
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2019 | Journal Article |
One-dimensional vs. two-dimensional proton transport processes at solid–liquid zinc-oxide–water interfaces
Hellström, M.; Quaranta, V. & Behler, J. (2019)
Chemical Science, 10(4) pp. 1232-1243. DOI: https://doi.org/10.1039/C8SC03033B
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2019 | Book Chapter
High-Dimensional Neural Network Potentials for Atomistic Simulations
Hellström, M.& Behler, J. (2019)
In:Pyzer-Knapp, Edward O.; Laino, Teodoro (Eds.), Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions pp. 49-59. Washington, DC: American Chemical Society. DOI: https://doi.org/10.1021/bk-2019-1326.ch003
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2019 | Journal Article |
Accurate Probabilities for Highly Activated Reaction of Polyatomic Molecules on Surfaces Using a High-Dimensional Neural Network Potential: CHD 3 + Cu(111)
Gerrits, N.; Shakouri, K.; Behler, J. & Kroes, G.-J. (2019)
The Journal of Physical Chemistry Letters, 10(8) pp. 1763-1768. DOI: https://doi.org/10.1021/acs.jpclett.9b00560
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2019 | Journal Article |
New Insights in the Catalytic Activity of Cobalt Orthophosphate Co3 (PO4)2 from Charge Density Analysis
Keil, H.; Hellström, M.; Stückl, C.; Herbst‐Irmer, R.; Behler, J. & Stalke, D. (2019)
Chemistry – A European Journal, 25(25) pp. 15786-15794. DOI: https://doi.org/10.1002/chem.201902303
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2019 | Journal Article |
Orbital-Dependent Electronic Friction Significantly Affects the Description of Reactive Scattering of N 2 from Ru(0001)
Spiering, P.; Shakouri, K.; Behler, J.; Kroes, G.-J. & Meyer, J. (2019)
The Journal of Physical Chemistry Letters, 10(11) pp. 2957-2962. DOI: https://doi.org/10.1021/acs.jpclett.9b00523
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2020 | Book Chapter
High-Dimensional Neural Network Potentials for Atomistic Simulations
Hellström, M.& Behler, J. (2020)
In:Schütt, Kristof T.; Chmiela, Stefan; von Lilienfeld, O. Anatole; Tkatchenko, Alexandre; Tsuda, Koji; Müller, Klaus-Robert (Eds.), Machine Learning Meets Quantum Physics pp. 253-275. Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-40245-7_13
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2021 | Journal Article | Research Paper
Insights into lithium manganese oxide–water interfaces using machine learning potentials
Eckhoff, M. & Behler, J. (2021)
The Journal of Chemical Physics, 155(24) pp. 244703. DOI: https://doi.org/10.1063/5.0073449
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2021 | Journal Article | Research Paper |
A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
Ko, T. W.; Finkler, J. A.; Goedecker, S. & Behler, J. (2021)
Nature Communications, 12(1). DOI: https://doi.org/10.1038/s41467-020-20427-2
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2021 | Journal Article |
An assessment of the structural resolution of various fingerprints commonly used in machine learning
Parsaeifard, B.; Sankar De, D.; Christensen, A. S; Faber, F. A; Kocer, E.; De, S. & Behler, J. et al. (2021)
Machine Learning, 2(1). DOI: https://doi.org/10.1088/2632-2153/abb212
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2021 | Journal Article |
A bin and hash method for analyzing reference data and descriptors in machine learning potentials
Paleico, M. L. & Behler, J. (2021)
Machine Learning, 2(3). DOI: https://doi.org/10.1088/2632-2153/abe663
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2021 | Journal Article |
Machine learning potentials for extended systems: a perspective
Behler, J. & Csányi, G. (2021)
The European Physical Journal. B, Condensed Matter and Complex Systems, 94(7) art. 142. DOI: https://doi.org/10.1140/epjb/s10051-021-00156-1
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2021 | Journal Article | Research Paper |
High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions
Eckhoff, M. & Behler, J. (2021)
npj Computational Materials, 7(1) art. 170. DOI: https://doi.org/10.1038/s41524-021-00636-z
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2022 | Journal Article
Coupled Cluster Molecular Dynamics of Condensed Phase Systems Enabled by Machine Learning Potentials: Liquid Water Benchmark
Daru, J.; Forbert, H.; Behler, J. & Marx, D. (2022)
Physical Review Letters, 129(22). DOI: https://doi.org/10.1103/PhysRevLett.129.226001
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2022 | Journal Article
Neural Network Potentials: A Concise Overview of Methods
Kocer, E.; Ko, T. W. & Behler, J. (2022)
Annual Review of Physical Chemistry, 73(1). DOI: https://doi.org/10.1146/annurev-physchem-082720-034254
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2022 | Journal Article
A Hessian-based assessment of atomic forces for training machine learning interatomic potentials
Herbold, M. & Behler, J. (2022)
The Journal of Chemical Physics, 156(11) pp. 114106. DOI: https://doi.org/10.1063/5.0082952
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2022 | Journal Article |
High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark
Shanavas Rasheeda, D.; Martín Santa Daría, A.; Schröder, B.; Mátyus, E. & Behler, J. (2022)
Physical Chemistry Chemical Physics, 24(48) pp. 29381-29392. DOI: https://doi.org/10.1039/D2CP03893E
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