| Year |
Citation |
Score |
| 2023 |
Abedi M, Behler J, Goldsmith CF. 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. PMID 37902963 DOI: 10.1021/acs.jctc.3c00469 |
0.319 |
|
| 2023 |
Ko TW, Finkler JA, Goedecker S, Behler J. Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding. Journal of Chemical Theory and Computation. 19: 3567-3579. PMID 37289440 DOI: 10.1021/acs.jctc.2c01146 |
0.602 |
|
| 2023 |
Herbold M, Behler J. Machine learning transferable atomic forces for large systems from underconverged molecular fragments. Physical Chemistry Chemical Physics : Pccp. 25: 12979-12989. PMID 37165873 DOI: 10.1039/d2cp05976b |
0.305 |
|
| 2022 |
Daru J, Forbert H, Behler J, Marx D. Coupled Cluster Molecular Dynamics of Condensed Phase Systems Enabled by Machine Learning Potentials: Liquid Water Benchmark. Physical Review Letters. 129: 226001. PMID 36493459 DOI: 10.1103/PhysRevLett.129.226001 |
0.649 |
|
| 2022 |
Shanavas Rasheeda D, Martín Santa Daría A, Schröder B, Mátyus E, Behler J. High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark. Physical Chemistry Chemical Physics : Pccp. 24: 29381-29392. PMID 36459127 DOI: 10.1039/d2cp03893e |
0.332 |
|
| 2022 |
Herbold M, Behler J. A Hessian-based assessment of atomic forces for training machine learning interatomic potentials. The Journal of Chemical Physics. 156: 114106. PMID 35317596 DOI: 10.1063/5.0082952 |
0.3 |
|
| 2022 |
Kocer E, Ko TW, Behler J. Neural Network Potentials: A Concise Overview of Methods. Annual Review of Physical Chemistry. 73: 163-186. PMID 34982580 DOI: 10.1146/annurev-physchem-082720-034254 |
0.31 |
|
| 2022 |
Eckhoff M, Behler J. Insights into lithium manganese oxide-water interfaces using machine learning potentials. The Journal of Chemical Physics. 155: 244703. PMID 34972388 DOI: 10.1063/5.0073449 |
0.34 |
|
| 2021 |
Behler J. Four Generations of High-Dimensional Neural Network Potentials. Chemical Reviews. PMID 33779150 DOI: 10.1021/acs.chemrev.0c00868 |
0.368 |
|
| 2021 |
Ko TW, Finkler JA, Goedecker S, Behler J. General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer. Accounts of Chemical Research. PMID 33513012 DOI: 10.1021/acs.accounts.0c00689 |
0.595 |
|
| 2021 |
Ko TW, Finkler JA, Goedecker S, Behler J. A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer. Nature Communications. 12: 398. PMID 33452239 DOI: 10.1038/s41467-020-20427-2 |
0.616 |
|
| 2020 |
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 : Pccp. PMID 32915176 DOI: 10.1039/D0Cp03462B |
0.431 |
|
| 2020 |
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. PMID 32787306 DOI: 10.1021/Acs.Jpclett.0C01739 |
0.37 |
|
| 2020 |
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. 153: 054704. PMID 32770878 DOI: 10.1063/5.0014876 |
0.427 |
|
| 2020 |
Lu D, Behler J, Li J. Accurate Global Potential Energy Surfaces for the H + CHOH Reaction by Neural Network Fitting with Permutation Invariance. The Journal of Physical Chemistry. A. PMID 32530628 DOI: 10.1021/Acs.Jpca.0C04182 |
0.399 |
|
| 2020 |
Zuo Y, Chen C, Li XG, Deng Z, Chen Y, Behler J, Csányi G, Shapeev AV, Thompson AP, Wood MA, Ong SP. A Performance and Cost Assessment of Machine Learning Interatomic Potentials. The Journal of Physical Chemistry. A. PMID 31916773 DOI: 10.1021/Acs.Jpca.9B08723 |
0.405 |
|
| 2020 |
Shao Y, Hellström M, Yllö A, Mindemark J, Hermansson K, Behler J, Zhang C. Temperature effects on the ionic conductivity in concentrated alkaline electrolyte solutions. Physical Chemistry Chemical Physics : Pccp. PMID 31895378 DOI: 10.1039/C9Cp06479F |
0.324 |
|
| 2020 |
Mangold C, Chen S, Barbalinardo G, Behler J, Pochet P, Termentzidis K, Han Y, Chaput L, Lacroix D, Donadio D. Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of MnxGey compounds Journal of Applied Physics. 127: 244901. DOI: 10.1063/5.0009550 |
0.639 |
|
| 2020 |
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. 124: 12682-12695. DOI: 10.1021/Acs.Jpcc.0C00559 |
0.392 |
|
| 2019 |
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. PMID 31743025 DOI: 10.1021/Acs.Jctc.9B00805 |
0.799 |
|
| 2019 |
Litman Y, Behler J, Rossi M. Temperature dependence of the vibrational spectrum of porphycene: a qualitative failure of classical-nuclei molecular dynamics. Faraday Discussions. PMID 31544185 DOI: 10.1039/C9Fd00056A |
0.354 |
|
| 2019 |
Stalke D, Keil H, Hellström M, Stückl C, Herbst-Irmer R, Behler J. New insights in the catalytic activity of cobalt orthophosphate Co3(PO4)2 from charge density analysis. Chemistry (Weinheim An Der Bergstrasse, Germany). PMID 31361370 DOI: 10.1002/Chem.201902303 |
0.331 |
|
| 2019 |
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. PMID 31091097 DOI: 10.1021/Acs.Jctc.8B01288 |
0.437 |
|
| 2019 |
Spiering P, Shakouri K, Behler J, Kroes GJ, Meyer J. Orbital-Dependent Electronic Friction Significantly Affects the Description of Reactive Scattering of N from Ru(0001). The Journal of Physical Chemistry Letters. PMID 31088059 DOI: 10.1021/Acs.Jpclett.9B00523 |
0.308 |
|
| 2019 |
Singraber A, Morawietz T, Behler J, Dellago C. Parallel Multistream Training of High-Dimensional Neural Network Potentials. Journal of Chemical Theory and Computation. PMID 30995035 DOI: 10.1021/Acs.Jctc.8B01092 |
0.797 |
|
| 2019 |
Gerrits N, Shakouri K, Behler J, Kroes GJ. Accurate Probabilities for Highly Activated Reaction of Polyatomic Molecules on Surfaces Using a High-Dimensional Neural Network Potential: CHD + Cu(111). The Journal of Physical Chemistry Letters. PMID 30922058 DOI: 10.1021/Acs.Jpclett.9B00560 |
0.41 |
|
| 2019 |
Hellström M, Quaranta V, Behler J. One-dimensional two-dimensional proton transport processes at solid-liquid zinc-oxide-water interfaces. Chemical Science. 10: 1232-1243. PMID 30774924 DOI: 10.1039/C8Sc03033B |
0.38 |
|
| 2019 |
Singraber A, Behler J, Dellago C. Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials. Journal of Chemical Theory and Computation. PMID 30677296 DOI: 10.1021/Acs.Jctc.8B00770 |
0.662 |
|
| 2019 |
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 : Pccp. PMID 30672927 DOI: 10.1039/C8Cp06919K |
0.423 |
|
| 2019 |
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. PMID 30610171 DOI: 10.1073/Pnas.1815117116 |
0.676 |
|
| 2019 |
Gabardi S, Sosso GG, Behler J, Bernasconi M. Priming effects in the crystallization of the phase change compound GeTe from atomistic simulations Faraday Discussions. 213: 287-301. PMID 30379974 DOI: 10.1039/C8Fd00101D |
0.325 |
|
| 2019 |
Bosoni E, Campi D, Donadio D, Sosso GC, Behler J, Bernasconi M. Atomistic simulations of thermal conductivity in GeTe nanowires Journal of Physics D: Applied Physics. 53: 054001. DOI: 10.1088/1361-6463/Ab5478 |
0.541 |
|
| 2019 |
Quaranta V, Behler J, Hellström M. Structure and Dynamics of the Liquid–Water/Zinc-Oxide Interface from Machine Learning Potential Simulations Journal of Physical Chemistry C. 123: 1293-1304. DOI: 10.1021/Acs.Jpcc.8B10781 |
0.408 |
|
| 2018 |
Shakouri K, Behler J, Meyer J, Kroes GJ. Analysis of Energy Dissipation Channels in a Benchmark System of Activated Dissociation: N on Ru(0001). The Journal of Physical Chemistry. C, Nanomaterials and Interfaces. 122: 23470-23480. PMID 30364480 DOI: 10.1021/Acs.Jpcc.8B06729 |
0.36 |
|
| 2018 |
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. PMID 30335385 DOI: 10.1021/Acs.Jpcb.8B06433 |
0.579 |
|
| 2018 |
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. 148: 241730. PMID 29960368 DOI: 10.1063/1.5024611 |
0.609 |
|
| 2018 |
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. 148: 241720. PMID 29960340 DOI: 10.1063/1.5012980 |
0.421 |
|
| 2018 |
Nguyen TT, Székely E, Imbalzano G, Behler J, Csányi G, Ceriotti M, Götz AW, Paesani F. Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions. The Journal of Chemical Physics. 148: 241725. PMID 29960316 DOI: 10.1063/1.5024577 |
0.595 |
|
| 2018 |
Singraber A, Morawietz T, Behler J, Dellago C. Density anomaly of water at negative pressures from first principles. Journal of Physics. Condensed Matter : An Institute of Physics Journal. PMID 29762140 DOI: 10.1088/1361-648X/Aac4F4 |
0.774 |
|
| 2018 |
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. 148: 102310. PMID 29544280 DOI: 10.1063/1.4996819 |
0.803 |
|
| 2017 |
Gastegger M, Behler J, Marquetand P. Machine learning molecular dynamics for the simulation of infrared spectra. Chemical Science. 8: 6924-6935. PMID 29147518 DOI: 10.1039/C7Sc02267K |
0.362 |
|
| 2017 |
Hellström M, Behler J. Surface phase diagram prediction from a minimal number of DFT calculations: redox-active adsorbates on zinc oxide. Physical Chemistry Chemical Physics : Pccp. 19: 28731-28748. PMID 29044257 DOI: 10.1039/C7Cp05182D |
0.371 |
|
| 2017 |
Behler J. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems. Angewandte Chemie (International Ed. in English). 56: 12828-12840. PMID 28520235 DOI: 10.1002/Anie.201703114 |
0.439 |
|
| 2017 |
Shakouri K, Behler J, Meyer J, Kroes GJ. Accurate Neural Network Description of Surface Phonons in Reactive Gas-Surface Dynamics: N2+Ru(0001). The Journal of Physical Chemistry Letters. PMID 28441867 DOI: 10.1021/Acs.Jpclett.7B00784 |
0.461 |
|
| 2017 |
Hellström M, Behler J. Proton-Transfer-Driven Water Exchange Mechanism in the Na Solvation Shell. The Journal of Physical Chemistry. B. 121: 4184-4190. PMID 28375608 DOI: 10.1021/Acs.Jpcb.7B01490 |
0.328 |
|
| 2017 |
Quaranta V, Hellström M, Behler J. Proton-Transfer Mechanisms at the Water-ZnO Interface: The Role of Presolvation. The Journal of Physical Chemistry Letters. 8: 1476-1483. PMID 28296415 DOI: 10.1021/Acs.Jpclett.7B00358 |
0.369 |
|
| 2017 |
Gabardi S, Baldi E, Bosoni E, Campi D, Caravati S, Sosso GC, Behler J, Bernasconi M. Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires Journal of Physical Chemistry C. 121: 23827-23838. DOI: 10.1021/Acs.Jpcc.7B09862 |
0.355 |
|
| 2017 |
Kondati Natarajan S, Behler J. Self-Diffusion of Surface Defects at Copper–Water Interfaces The Journal of Physical Chemistry C. 121: 4368-4383. DOI: 10.1021/Acs.Jpcc.6B12657 |
0.359 |
|
| 2016 |
Kapil V, Behler J, Ceriotti M. High order path integrals made easy. The Journal of Chemical Physics. 145: 234103. PMID 28010075 DOI: 10.1063/1.4971438 |
0.603 |
|
| 2016 |
Behler J. Perspective: Machine learning potentials for atomistic simulations. The Journal of Chemical Physics. 145: 170901. PMID 27825224 DOI: 10.1063/1.4966192 |
0.381 |
|
| 2016 |
Hellström M, Behler J. Structure of aqueous NaOH solutions: insights from neural-network-based molecular dynamics simulations. Physical Chemistry Chemical Physics : Pccp. 19: 82-96. PMID 27805193 DOI: 10.1039/C6Cp06547C |
0.368 |
|
| 2016 |
Natarajan SK, Behler J. Neural network molecular dynamics simulations of solid-liquid interfaces: water at low-index copper surfaces. Physical Chemistry Chemical Physics : Pccp. 18: 28704-28725. PMID 27722603 DOI: 10.1039/C6Cp05711J |
0.403 |
|
| 2016 |
Hellström M, Behler J. Concentration-Dependent Proton Transfer Mechanisms in Aqueous NaOH Solutions: From Acceptor-Driven to Donor-Driven and Back. The Journal of Physical Chemistry Letters. 7: 3302-6. PMID 27504986 DOI: 10.1021/Acs.Jpclett.6B01448 |
0.318 |
|
| 2016 |
Morawietz T, Singraber A, Dellago C, Behler J. How van der Waals interactions determine the unique properties of water. Proceedings of the National Academy of Sciences of the United States of America. PMID 27402761 DOI: 10.1073/Pnas.1602375113 |
0.793 |
|
| 2016 |
Gastegger M, Kauffmann C, Behler J, Marquetand P. Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes. The Journal of Chemical Physics. 144: 194110. PMID 27208939 DOI: 10.1063/1.4950815 |
0.457 |
|
| 2016 |
Cheng B, Behler J, Ceriotti M. Nuclear Quantum Effects in Water at the Triple Point: Using Theory as a Link Between Experiments. The Journal of Physical Chemistry Letters. PMID 27203358 DOI: 10.1021/Acs.Jpclett.6B00729 |
0.537 |
|
| 2016 |
Sosso GC, Behler J, Bernasconi M. Atomic mobility in the overheated amorphous GeTe compound for phase change memories Physica Status Solidi (a). 213: 329-334. DOI: 10.1002/Pssa.201532378 |
0.318 |
|
| 2015 |
Seema P, Behler J, Marx D. Peeling by Nanomechanical Forces: A Route to Selective Creation of Surface Structures. Physical Review Letters. 115: 036102. PMID 26230805 DOI: 10.1103/Physrevlett.115.036102 |
0.656 |
|
| 2015 |
Kondati Natarajan S, Morawietz T, Behler J. Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials. Physical Chemistry Chemical Physics : Pccp. 17: 8356-71. PMID 25436835 DOI: 10.1039/C4Cp04751F |
0.781 |
|
| 2015 |
Campi D, Donadio D, Sosso GC, Behler J, Bernasconi M. Electron-phonon interaction and thermal boundary resistance at the crystal-amorphous interface of the phase change compound GeTe Journal of Applied Physics. 117: 15304. DOI: 10.1063/1.4904910 |
0.593 |
|
| 2015 |
Sosso GC, Salvalaglio M, Behler J, Bernasconi M, Parrinello M. Heterogeneous crystallization of the phase change material GeTe via atomistic simulations Journal of Physical Chemistry C. 119: 6428-6434. DOI: 10.1021/Acs.Jpcc.5B00296 |
0.722 |
|
| 2015 |
Behler J. Constructing high‐dimensional neural network potentials: A tutorial review International Journal of Quantum Chemistry. 115: 1032-1050. DOI: 10.1002/Qua.24890 |
0.455 |
|
| 2014 |
Sosso GC, Colombo J, Behler J, Del Gado E, Bernasconi M. Dynamical heterogeneity in the supercooled liquid state of the phase change material GeTe. The Journal of Physical Chemistry. B. 118: 13621-8. PMID 25356792 DOI: 10.1021/Jp507361F |
0.391 |
|
| 2014 |
Behler J. Representing potential energy surfaces by high-dimensional neural network potentials. Journal of Physics: Condensed Matter. 26: 183001-183001. PMID 24758952 DOI: 10.1088/0953-8984/26/18/183001 |
0.492 |
|
| 2014 |
Handley CM, Behler J. Next generation interatomic potentials for condensed systems European Physical Journal B. 87. DOI: 10.1140/Epjb/E2014-50070-0 |
0.412 |
|
| 2013 |
Seema P, Behler J, Marx D. Force-induced mechanical response of molecule-metal interfaces: molecular nanomechanics of propanethiolate self-assembled monolayers on Au(111). Physical Chemistry Chemical Physics : Pccp. 15: 16001-11. PMID 23959524 DOI: 10.1039/C3Cp52181H |
0.639 |
|
| 2013 |
Morawietz T, Behler J. A density-functional theory-based neural network potential for water clusters including van der Waals corrections. The Journal of Physical Chemistry. A. 117: 7356-66. PMID 23557541 DOI: 10.1021/Jp401225B |
0.775 |
|
| 2013 |
Morawietz T, Behler J. A Full-Dimensional Neural Network Potential-Energy Surface for Water Clusters up to the Hexamer Zeitschrift FüR Physikalische Chemie. 227: 1559-1581. DOI: 10.1524/Zpch.2013.0384 |
0.767 |
|
| 2013 |
Seema P, Behler J, Marx D. Adsorption of Methanethiolate and Atomic Sulfur at the Cu(111) Surface: A Computational Study Journal of Physical Chemistry C. 117: 337-348. DOI: 10.1021/Jp309728W |
0.655 |
|
| 2013 |
Artrith N, Hiller B, Behler J. Front Cover: Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide (Phys. Status Solidi B 6/2013) Physica Status Solidi (B). 250. DOI: 10.1002/Pssb.201370539 |
0.72 |
|
| 2012 |
Jose KVJ, Artrith N, Behler J. Construction of high-dimensional neural network potentials using environment-dependent atom pairs Journal of Chemical Physics. 136. PMID 22612084 DOI: 10.1063/1.4712397 |
0.773 |
|
| 2012 |
Eshet H, Khaliullin RZ, Kühne TD, Behler J, Parrinello M. Microscopic origins of the anomalous melting behavior of sodium under high pressure. Physical Review Letters. 108: 115701. PMID 22540486 DOI: 10.1103/Physrevlett.108.115701 |
0.685 |
|
| 2012 |
Morawietz T, Sharma V, Behler J. A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges. The Journal of Chemical Physics. 136: 064103. PMID 22360165 DOI: 10.1063/1.3682557 |
0.785 |
|
| 2012 |
Eshet H, Khaliullin RZ, Kühne TD, Behler J, Parrinello M. Microscopic origins of the anomalous melting behavior of sodium under high pressure Physical Review Letters. 108. DOI: 10.1103/PhysRevLett.108.115701 |
0.609 |
|
| 2012 |
Sosso GC, Donadio D, Caravati S, Behler J, Bernasconi M. Thermal transport in phase-change materials from atomistic simulations Physical Review B. 86: 104301. DOI: 10.1103/Physrevb.86.104301 |
0.602 |
|
| 2012 |
Artrith N, Morawietz T, Behler J. Erratum: High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide [Phys. Rev. B83, 153101 (2011)] Physical Review B. 86. DOI: 10.1103/Physrevb.86.079914 |
0.777 |
|
| 2012 |
Sosso GC, Miceli G, Caravati S, Behler J, Bernasconi M. Neural network interatomic potential for the phase change material GeTe Physical Review B. 85: 174103. DOI: 10.1103/Physrevb.85.174103 |
0.448 |
|
| 2012 |
Artrith N, Behler J. High-dimensional neural network potentials for metal surfaces: A prototype study for copper Physical Review B - Condensed Matter and Materials Physics. 85. DOI: 10.1103/Physrevb.85.045439 |
0.768 |
|
| 2012 |
Sosso GC, Behler J, Bernasconi M. Breakdown of Stokes–Einstein relation in the supercooled liquid state of phase change materials† Physica Status Solidi B-Basic Solid State Physics. 249: 1880-1885. DOI: 10.1002/Pssb.201200355 |
0.314 |
|
| 2011 |
Behler J. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Physical Chemistry Chemical Physics : Pccp. 13: 17930-55. PMID 21915403 DOI: 10.1039/C1Cp21668F |
0.484 |
|
| 2011 |
Khaliullin RZ, Eshet H, Kühne TD, Behler J, Parrinello M. Nucleation mechanism for the direct graphite-to-diamond phase transition. Nature Materials. 10: 693-7. PMID 21785417 DOI: 10.1038/Nmat3078 |
0.692 |
|
| 2011 |
Behler J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. The Journal of Chemical Physics. 134: 074106. PMID 21341827 DOI: 10.1063/1.3553717 |
0.509 |
|
| 2011 |
Artrith N, Morawietz T, Behler J. High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide Physical Review B - Condensed Matter and Materials Physics. 83. DOI: 10.1103/Physrevb.83.153101 |
0.811 |
|
| 2011 |
Khaliullin RZ, Eshet H, Kühne TD, Behler J, Parrinello M. Nucleation mechanism for the direct graphite-to-diamond phase transition Nature Materials. 10: 693-697. DOI: 10.1038/nmat3078 |
0.616 |
|
| 2010 |
Eshet H, Khaliullin RZ, Kühne TD, Behler J, Parrinello M. Ab initio quality neural-network potential for sodium Physical Review B - Condensed Matter and Materials Physics. 81. DOI: 10.1103/Physrevb.81.184107 |
0.726 |
|
| 2010 |
Khaliullin RZ, Eshet H, Kühne TD, Behler J, Parrinello M. Graphite-diamond phase coexistence study employing a neural-network mapping of the ab initio potential energy surface Physical Review B - Condensed Matter and Materials Physics. 81. DOI: 10.1103/Physrevb.81.100103 |
0.743 |
|
| 2010 |
Carbogno C, Behler J, Reuter K, Groß A. Signatures of nonadiabaticO2dissociation at Al(111): First-principles fewest-switches study Physical Review B. 81. DOI: 10.1103/Physrevb.81.035410 |
0.64 |
|
| 2008 |
Carbogno C, Behler J, Grob A, Reuter K. Fingerprints for spin-selection rules in the interaction dynamics of O2 at Al(111). Physical Review Letters. 101: 096104. PMID 18851627 DOI: 10.1103/Physrevlett.101.096104 |
0.652 |
|
| 2008 |
Behler J, Martonák R, Donadio D, Parrinello M. Metadynamics simulations of the high-pressure phases of silicon employing a high-dimensional neural network potential. Physical Review Letters. 100: 185501. PMID 18518388 DOI: 10.1103/Physrevlett.100.185501 |
0.739 |
|
| 2008 |
Behler J, Reuter K, Scheffler M. Nonadiabatic effects in the dissociation of oxygen molecules at the Al(111) surface Physical Review B. 77. DOI: 10.1103/Physrevb.77.115421 |
0.73 |
|
| 2008 |
Behler J, Martoňák R, Donadio D, Parrinello M. Pressure-induced phase transitions in silicon studied by neural network-based metadynamics simulations Physica Status Solidi (B) Basic Research. 245: 2618-2629. DOI: 10.1002/Pssb.200844219 |
0.75 |
|
| 2007 |
Behler J, Lorenz S, Reuter K. Representing molecule-surface interactions with symmetry-adapted neural networks. The Journal of Chemical Physics. 127: 014705. PMID 17627362 DOI: 10.1063/1.2746232 |
0.703 |
|
| 2007 |
Behler J, Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Physical Review Letters. 98: 146401. PMID 17501293 DOI: 10.1103/Physrevlett.98.146401 |
0.679 |
|
| 2007 |
Cano-Cortés L, Dolfen A, Merino J, Behler J, Delley B, Reuter K, Koch E. Spectral broadening due to long-range Coulomb interactions in the molecular metal TTF-TCNQ The European Physical Journal B. 56: 173-176. DOI: 10.1140/Epjb/E2007-00110-Y |
0.655 |
|
| 2007 |
Behler J, Delley B, Reuter K, Scheffler M. Nonadiabatic potential-energy surfaces by constrained density-functional theory Physical Review B. 75. DOI: 10.1103/Physrevb.75.115409 |
0.762 |
|
| 2006 |
Behler J, Reuter K, Scheffler M. Behler, Reuter, and Scheffler Reply: Physical Review Letters. 96. DOI: 10.1103/Physrevlett.96.079802 |
0.659 |
|
| 2005 |
Behler J, Delley B, Lorenz S, Reuter K, Scheffler M. Dissociation of O2 at Al(111): the role of spin selection rules. Physical Review Letters. 94: 036104. PMID 15698287 DOI: 10.1103/Physrevlett.94.036104 |
0.708 |
|
| 2005 |
Ratsch C, Fielicke A, Kirilyuk A, Behler J, Von Helden G, Meijer G, Scheffler M. Structure determination of small vanadium clusters by density-functional theory in comparison with experimental far-infrared spectra Journal of Chemical Physics. 122. DOI: 10.1063/1.1862621 |
0.593 |
|
| 2004 |
Fielicke A, Kirilyuk A, Ratsch C, Behler J, Scheffler M, von Helden G, Meijer G. Structure determination of isolated metal clusters via far-infrared spectroscopy. Physical Review Letters. 93: 023401. PMID 15323913 DOI: 10.1103/Physrevlett.93.023401 |
0.566 |
|
| 2002 |
Ludwig R, Behler J, Klink B, Weinhold F. Molecular composition of liquid sulfur. Angewandte Chemie (International Ed. in English). 41: 3199-202. PMID 12207388 DOI: 10.1002/1521-3773(20020902)41:17<3199::Aid-Anie3199>3.0.Co;2-9 |
0.383 |
|
| 2001 |
Behler J, Price DW, Drew MGB. Water structuring properties of carbohydrates, molecular dynamics studies on 1,5-anhydro-D-fructose Physical Chemistry Chemical Physics. 3: 588-601. DOI: 10.1039/B007899I |
0.334 |
|
| Low-probability matches (unlikely to be authored by this person) |
| 2020 |
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. 153: 164107. PMID 33138439 DOI: 10.1063/5.0021452 |
0.297 |
|
| 2016 |
Lu D, Qi J, Yang M, Behler J, Song H, Li J. Mode specific dynamics in the H2 + SH → H + H2S reaction. Physical Chemistry Chemical Physics : Pccp. PMID 27730236 DOI: 10.1039/C6Cp05780B |
0.291 |
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| 2020 |
Eckhoff M, Blöchl PE, Behler J. Hybrid density functional theory benchmark study on lithium manganese oxides Physical Review B. 101: 205113. DOI: 10.1103/Physrevb.101.205113 |
0.284 |
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| 2016 |
Behler J. Erratum: "Perspective: Machine learning potentials for atomistic simulations" [J. Chem. Phys. 145, 170901 (2016)]. The Journal of Chemical Physics. 145: 219901. PMID 28799339 DOI: 10.1063/1.4971792 |
0.276 |
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| 2020 |
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 : Pccp. PMID 33290481 DOI: 10.1039/d0cp90265a |
0.262 |
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| 2019 |
Keil H, Hellström M, Stückl C, Herbst‐Irmer R, Behler J, Stalke D. Cover Feature: New Insights into the Catalytic Activity of Cobalt Orthophosphate Co
3
(PO
4
)
2
from Charge Density Analysis (Chem. Eur. J. 69/2019) Chemistry – a European Journal. 25: 15704-15704. DOI: 10.1002/Chem.201904453 |
0.252 |
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| 2020 |
Paleico ML, Behler J. A flexible and adaptive grid algorithm for global optimization utilizing basin hopping Monte Carlo. The Journal of Chemical Physics. 152: 094109. PMID 33480732 DOI: 10.1063/1.5142363 |
0.25 |
|
| 2013 |
Sosso GC, Miceli G, Caravati S, Giberti F, Behler J, Bernasconi M. Fast Crystallization of the Phase Change Compound GeTe by Large-Scale Molecular Dynamics Simulations. The Journal of Physical Chemistry Letters. 4: 4241-6. PMID 26296172 DOI: 10.1021/jz402268v |
0.237 |
|
| 2015 |
Gabardi S, Caravati S, Sosso GC, Behler J, Bernasconi M. Microscopic origin of resistance drift in the amorphous state of the phase-change compound GeTe Physical Review B. 92: 54201. DOI: 10.1103/Physrevb.92.054201 |
0.228 |
|
| 2017 |
Behler J. Hochdimensionale neuronale Netze für Potentialhyperflächen großer molekularer und kondensierter Systeme Angewandte Chemie. 129: 13006-13020. DOI: 10.1002/Ange.201703114 |
0.213 |
|
| 2002 |
Ludwig R, Behler J, Klink B, Weinhold F. Die molekulare Zusammensetzung des fl��ssigen Schwefels Angewandte Chemie. 114: 3331-3335. DOI: 10.1002/1521-3757(20020902)114:17<3331::Aid-Ange3331>3.0.Co;2-M |
0.18 |
|
| 2021 |
Dragoni D, Behler J, Bernasconi M. Mechanism of amorphous phase stabilization in ultrathin films of monoatomic phase change material. Nanoscale. 13: 16146-16155. PMID 34542138 DOI: 10.1039/d1nr03432d |
0.158 |
|
| 2023 |
Tokita AM, Behler J. How to train a neural network potential. The Journal of Chemical Physics. 159. PMID 38127396 DOI: 10.1063/5.0160326 |
0.12 |
|
| 2023 |
Liebetrau M, Dorenkamp Y, Bünermann O, Behler J. Hydrogen atom scattering at the AlO(0001) surface: a combined experimental and theoretical study. Physical Chemistry Chemical Physics : Pccp. PMID 38126723 DOI: 10.1039/d3cp04729f |
0.12 |
|
| 2023 |
Herbst-Irmer R, Wang X, Haberstock L, Köhne I, Oswald R, Behler J, Stalke D. A new polymorph of white phosphorus at ambient conditions. Iucrj. 10: 766-771. PMID 37910142 DOI: 10.1107/S2052252523009247 |
0.066 |
|
| 2016 |
Behler J. Machine-Learning-Potenziale Nachrichten Aus Der Chemie. 64: 323-325. DOI: 10.1002/nadc.20164047447 |
0.047 |
|
| Hide low-probability matches. |