Prof. Dr. Jörg Behler

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  • 2022 | Journal Article | Research Paper | 
    ​ ​Kulik HJ, Hammerschmidt T, Schmidt J, Botti S, Marques MAL, Boley M, et al. ​Roadmap on Machine learning in electronic structure​. ​​Electronic Structure. ​2022;​4​(2). ​doi:10.1088/2516-1075/ac572f. 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Weinreich J, Paleico ML, Behler J. ​Properties of α-Brass Nanoparticles II: Structure and Composition​. ​​The Journal of Physical Chemistry. C, Nanomaterials and interfaces. ​2021;​125​(27):​​14897​-14909​. ​doi:10.1021/acs.jpcc.1c02314. 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Ko TW, Finkler JA, Goedecker S, Behler J. ​General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer​. ​​Accounts of Chemical Research. ​2021;​54​(4):​​808​-817​. ​doi:10.1021/acs.accounts.0c00689. 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Behler J. ​Four Generations of High-Dimensional Neural Network Potentials​. ​​Chemical Reviews. ​2021;​121​(16):​​10037​-10072​. ​doi:10.1021/acs.chemrev.0c00868. 
    ​Details  DOI 
  • 2021 | Journal Article | 
    ​ ​Dragoni D, Behler J, Bernasconi M. ​Mechanism of amorphous phase stabilization in ultrathin films of monoatomic phase change material​. ​​Nanoscale. ​2021;. ​doi:10.1039/D1NR03432D. 
    ​Details  DOI 
  • 2020 | Preprint
    ​ ​Schönewald F, Eckhoff M, Baumung M, Risch M, Blöchl PE, Behler J, et al. ​A criticial view on e$ occupancy as a descriptor for oxygen evolution catalytic activity in LiMn$ nanoparticles​. ​​2020. 
    ​Details  arXiv 
  • 2020 | Journal Article | Research Paper
    ​ ​Eckhoff M, Blöchl PE, Behler J. ​Hybrid density functional theory benchmark study on lithium manganese oxides​. ​​Physical Review B. ​2020;​101​(20). ​doi:10.1103/PhysRevB.101.205113. 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Litman Y, Behler J, Rossi M. ​Temperature dependence of the vibrational spectrum of porphycene: a qualitative failure of classical-nuclei molecular dynamics​. ​​Faraday Discussions. ​2020;​221​:​​526​-546​. ​doi:10.1039/c9fd00056a. 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper
    ​ ​Eckhoff M, Lausch KN, Blöchl PE, Behler J. ​Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels​. ​​The Journal of Chemical Physics. ​2020;​153​(16):​​164107​. ​doi:10.1063/5.0021452. 
    ​Details  DOI 
  • 2020 | Journal Article | Erratum
    ​ ​Li J, Song K, Behler J. ​Correction: A critical comparison of neural network potentials for molecular reaction dynamics with exact permutation symmetry​. ​​Physical Chemistry Chemical Physics. ​2020;​22​(47):​​27914​-27915​. ​doi:10.1039/d0cp90265a. 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Mangold C, Chen S, Barbalinardo G, Behler J, Pochet P, Termentzidis K, et al. ​Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of Mn x Ge y compounds​. ​​Journal of Applied Physics. ​2020;​127​(24):​​244901​. ​doi:10.1063/5.0009550. 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Ghorbanfekr H, Behler J, Peeters FM. ​Insights into Water Permeation through hBN Nanocapillaries by Ab Initio Machine Learning Molecular Dynamics Simulations​. ​​The Journal of Physical Chemistry Letters. ​2020;​11​(17):​​7363​-7370​. ​doi:10.1021/acs.jpclett.0c01739. 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Paleico ML, Behler J. ​A flexible and adaptive grid algorithm for global optimization utilizing basin hopping Monte Carlo​. ​​The Journal of Chemical Physics. ​2020;​152​(9):​​094109​. ​doi:10.1063/1.5142363. 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper
    ​ ​Eckhoff M, Schönewald F, Risch M, Volkert CA, Blöchl PE, Behler J. ​Closing the gap between theory and experiment for lithium manganese oxide spinels using a high-dimensional neural network potential​. ​​Physical Review B. ​2020;​102​(17). ​doi:10.1103/PhysRevB.102.174102. 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Paleico ML, Behler J. ​Global optimization of copper clusters at the ZnO(101¯0) surface using a DFT-based neural network potential and genetic algorithms​. ​​The Journal of Chemical Physics. ​2020;​153​(5):​​054704​. ​doi:10.1063/5.0014876. 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Shao Y, Hellström M, Yllö A, Mindemark J, Hermansson K, Behler J, et al. ​Temperature effects on the ionic conductivity in concentrated alkaline electrolyte solutions​. ​​Physical Chemistry Chemical Physics. ​2020;​22​(19):​​10426​-10430​. ​doi:10.1039/c9cp06479f. 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Weinreich J, Römer A, Paleico ML, Behler J. ​Properties of α-Brass Nanoparticles. 1. Neural Network Potential Energy Surface​. ​​The Journal of Physical Chemistry C. ​2020;​124​(23):​​12682​-12695​. ​doi:10.1021/acs.jpcc.0c00559. 
    ​Details  DOI 
  • 2020 | Journal Article
    ​ ​Lu D, Behler J, Li J. ​Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance​. ​​The Journal of Physical Chemistry A. ​2020;​124​(28):​​5737​-5745​. ​doi:10.1021/acs.jpca.0c04182. 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper | 
    ​ ​Wille S, Jiang H, Bünermann O, Wodtke AM, Behler J, Kandratsenka A. ​An experimentally validated neural-network potential energy surface for H-atom on free-standing graphene in full dimensionality​. ​​Physical Chemistry Chemical Physics. ​2020;​22​(45):​​26113​-26120​. ​doi:10.1039/d0cp03462b. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Keil H, Hellström M, Stückl C, Herbst‐Irmer R, Behler J, Stalke D. ​New Insights into the Catalytic Activity of Cobalt Orthophosphate Co 3 (PO 4 ) 2 from Charge Density Analysis​. ​​Chemistry – A European Journal. ​2019;​25​(69):​​15786​-15794​. ​doi:10.1002/chem.v25.69. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Schran C, Behler J, Marx D. ​Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground​. ​​Journal of Chemical Theory and Computation. ​2019;​16​(1):​​88​-99​. ​doi:10.1021/acs.jctc.9b00805. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Singraber A, Behler J, Dellago C. ​Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials​. ​​Journal of Chemical Theory and Computation. ​2019;​15​(3):​​1827​-1840​. ​doi:10.1021/acs.jctc.8b00770. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Singraber A, Morawietz T, Behler J, Dellago C. ​Parallel Multistream Training of High-Dimensional Neural Network Potentials​. ​​Journal of Chemical Theory and Computation. ​2019;​15​(5):​​3075​-3092​. ​doi:10.1021/acs.jctc.8b01092. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Eckhoff M, Behler J. ​From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5​. ​​Journal of Chemical Theory and Computation. ​2019;​15​(6):​​3793​-3809​. ​doi:10.1021/acs.jctc.8b01288. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Gabardi S, Sosso GG, Behler J, Bernasconi M. ​Priming effects in the crystallization of the phase change compound GeTe from atomistic simulations​. ​​Faraday Discussions. ​2019;​213​:​​287​-301​. ​doi:10.1039/C8FD00101D. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Bosoni E, Campi D, Donadio D, Sosso , Behler J, Bernasconi M. ​Atomistic simulations of thermal conductivity in GeTe nanowires​. ​​Journal of Physics D: Applied Physics. ​2019;​53​(5):​​054001​. ​doi:10.1088/1361-6463/ab5478. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Zuo Y, Chen C, Li X, Deng Z, Chen Y, Behler J, et al. ​Performance and Cost Assessment of Machine Learning Interatomic Potentials​. ​​The Journal of Physical Chemistry A. ​2019;​124​(4):​​731​-745​. ​doi:10.1021/acs.jpca.9b08723. 
    ​Details  DOI 
  • 2019 | Journal Article | 
    ​ ​Li J, Song K, Behler J. ​A critical comparison of neural network potentials for molecular reaction dynamics with exact permutation symmetry​. ​​Physical Chemistry Chemical Physics. ​2019;​21​(19):​​9672​-9682​. ​doi:10.1039/C8CP06919K. 
    ​Details  DOI 
  • 2019 | Journal Article
    ​ ​Cheng B, Engel EA, Behler J, Dellago C, Ceriotti M. ​Ab initio thermodynamics of liquid and solid water​. ​​Proceedings of the National Academy of Sciences of the United States of America. ​2019;​116​(4):​​1110​-1115​. ​doi:10.1073/pnas.1815117116. 
    ​Details  DOI  PMID  PMC  arXiv 
  • 2018 | Journal Article
    ​ ​Hellström M, Ceriotti M, Behler J. ​Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations​. ​​The Journal of Physical Chemistry B. ​2018;​122​(44):​​10158​-10171​. ​doi:10.1021/acs.jpcb.8b06433. 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Quaranta V, Behler J, Hellström M. ​Structure and Dynamics of the Liquid–Water/Zinc-Oxide Interface from Machine Learning Potential Simulations​. ​​The Journal of Physical Chemistry C. ​2018;​123​(2):​​1293​-1304​. ​doi:10.1021/acs.jpcc.8b10781. 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Schran C, Uhl F, Behler J, Marx D. ​High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium​. ​​The Journal of Chemical Physics. ​2018;​148​(10):​​102310​. ​doi:10.1063/1.4996819. 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Quaranta V, Hellström M, Behler J, Kullgren J, Mitev PD, Hermansson K. ​Maximally resolved anharmonic OH vibrational spectrum of the water/ZnO(101¯0) interface from a high-dimensional neural network potential​. ​​The Journal of Chemical Physics. ​2018;​148​(24):​​241720​. ​doi:10.1063/1.5012980. 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Imbalzano G, Anelli A, Giofré D, Klees S, Behler J, Ceriotti M. ​Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials​. ​​The Journal of Chemical Physics. ​2018;​148​(24):​​241730​. ​doi:10.1063/1.5024611. 
    ​Details  DOI 
  • 2018 | Journal Article
    ​ ​Nguyen TT, Székely E, Imbalzano G, Behler J, Csányi G, Ceriotti M, et al. ​Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions​. ​​The Journal of Chemical Physics. ​2018;​148​(24):​​241725​. ​doi:10.1063/1.5024577. 
    ​Details  DOI 
  • 2018 | Journal Article | 
    ​ ​Singraber A, Morawietz T, Behler J, Dellago C. ​Density anomaly of water at negative pressures from first principles​. ​​Journal of Physics: Condensed Matter. ​2018;​30​(25):​​254005​. ​doi:10.1088/1361-648X/aac4f4. 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Gabardi S, Baldi E, Bosoni E, Campi D, Caravati S, Sosso GC, et al. ​Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires​. ​​The Journal of Physical Chemistry C. ​2017;​121​(42):​​23827​-23838​. ​doi:10.1021/acs.jpcc.7b09862. 
    ​Details  DOI 
  • 2017 | Journal Article
    ​ ​Behler J. ​First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems​. ​​Angewandte Chemie International Edition. ​2017;​56​(42):​​12828​-12840​. ​doi:10.1002/anie.201703114. 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​Gastegger M, Behler J, Marquetand P. ​Machine learning molecular dynamics for the simulation of infrared spectra​. ​​Chemical Science. ​2017;​8​(10):​​6924​-6935​. ​doi:10.1039/C7SC02267K. 
    ​Details  DOI 
  • 2017 | Journal Article | 
    ​ ​Shakouri K, Behler J, Meyer J, Kroes, Geert-Jan. ​Accurate Neural Network Description of Surface Phonons in Reactive Gas-Surface Dynamics: N-2 + Ru(0001)​. ​​The Journal of Physical Chemistry Letters. ​2017;​8​(10):​​2131​-2136​. ​doi:10.1021/acs.jpclett.7b00784. 
    ​Details  DOI  PMID  PMC  WoS 

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