Jörg Behler
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Jörg Behlerchemistry Degrees
Chemistry
#4139
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#5202
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Computational Chemistry
#43
World Rank
#43
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Chemistry Physics
Jörg Behler's Degrees
- PhD Physics University of Stuttgart
- Masters Physics University of Stuttgart
- Bachelors Physics University of Stuttgart
Why Is Jörg Behler Influential?
(Suggest an Edit or Addition)Jörg Behler's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Generalized neural-network representation of high-dimensional potential-energy surfaces. (2007) (1870)
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials. (2011) (887)
- Perspective: Machine learning potentials for atomistic simulations. (2016) (794)
- Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. (2011) (514)
- Constructing high‐dimensional neural network potentials: A tutorial review (2015) (485)
- First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems. (2017) (373)
- A Performance and Cost Assessment of Machine Learning Interatomic Potentials. (2019) (321)
- Representing potential energy surfaces by high-dimensional neural network potentials (2014) (273)
- How van der Waals interactions determine the unique properties of water (2016) (266)
- Nucleation mechanism for the direct graphite-to-diamond phase transition. (2011) (263)
- Machine learning molecular dynamics for the simulation of infrared spectra† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02267k (2017) (252)
- High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide (2011) (223)
- Dissociation of O2 at Al(111): the role of spin selection rules. (2004) (203)
- Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials. (2018) (196)
- High-dimensional neural network potentials for metal surfaces: A prototype study for copper (2012) (188)
- Metadynamics simulations of the high-pressure phases of silicon employing a high-dimensional neural network potential. (2008) (170)
- Ab initio thermodynamics of liquid and solid water (2018) (161)
- A density-functional theory-based neural network potential for water clusters including van der Waals corrections. (2013) (149)
- Neural network interatomic potential for the phase change material GeTe (2012) (148)
- Four Generations of High-Dimensional Neural Network Potentials. (2021) (145)
- Structure determination of isolated metal clusters via far-infrared spectroscopy. (2004) (136)
- Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions. (2018) (131)
- Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials. (2019) (128)
- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer (2020) (126)
- Representing molecule-surface interactions with symmetry-adapted neural networks. (2007) (121)
- Fast Crystallization of the Phase Change Compound GeTe by Large-Scale Molecular Dynamics Simulations. (2013) (120)
- Neural network potentials for metals and oxides – First applications to copper clusters at zinc oxide (2013) (106)
- Construction of high-dimensional neural network potentials using environment-dependent atom pairs. (2012) (105)
- A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges. (2012) (104)
- Accurate Neural Network Description of Surface Phonons in Reactive Gas–Surface Dynamics: N2 + Ru(0001) (2017) (103)
- Neural network molecular dynamics simulations of solid-liquid interfaces: water at low-index copper surfaces. (2016) (98)
- Nonadiabatic effects in the dissociation of oxygen molecules at the Al(111) surface (2007) (94)
- Ab initio quality neural-network potential for sodium (2010) (93)
- Parallel Multistream Training of High-Dimensional Neural Network Potentials. (2019) (92)
- Graphite-diamond phase coexistence study employing a neural-network mapping of the ab initio potential energy surface (2010) (90)
- Proton-Transfer Mechanisms at the Water-ZnO Interface: The Role of Presolvation. (2017) (83)
- Breakdown of Stokes–Einstein relation in the supercooled liquid state of phase change materials (2012) (70)
- Nonadiabatic potential-energy surfaces by constrained density-functional theory (2006) (70)
- Next generation interatomic potentials for condensed systems (2014) (66)
- Signatures of nonadiabatic O2 dissociation at Al(111): First-principles fewest-switches study (2009) (65)
- Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground (2019) (64)
- Fingerprints for spin-selection rules in the interaction dynamics of O2 at Al(111). (2008) (63)
- Microscopic origin of resistance drift in the amorphous state of the phase-change compound GeTe (2015) (61)
- Structure determination of small vanadium clusters by density-functional theory in comparison with experimental far-infrared spectra. (2005) (60)
- Pressure‐induced phase transitions in silicon studied by neural network‐based metadynamics simulations (2008) (56)
- Concentration-Dependent Proton Transfer Mechanisms in Aqueous NaOH Solutions: From Acceptor-Driven to Donor-Driven and Back. (2016) (55)
- Nuclear Quantum Effects in Water at the Triple Point: Using Theory as a Link Between Experiments. (2016) (52)
- Thermal transport in phase-change materials from atomistic simulations (2012) (51)
- Microscopic origins of the anomalous melting behavior of sodium under high pressure. (2011) (50)
- Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials. (2015) (49)
- Dynamical heterogeneity in the supercooled liquid state of the phase change material GeTe. (2014) (49)
- From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5. (2019) (47)
- Structure of aqueous NaOH solutions: insights from neural-network-based molecular dynamics simulations. (2016) (46)
- High order path integrals made easy. (2016) (43)
- Accurate Probabilities for Highly Activated Reaction of Polyatomic Molecules on Surfaces Using a High-Dimensional Neural Network Potential: CHD3 + Cu(111) (2019) (42)
- Electron-phonon interaction and thermal boundary resistance at the crystal-amorphous interface of the phase change compound GeTe (2015) (40)
- Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes. (2016) (38)
- General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer. (2021) (37)
- Machine learning potentials for extended systems: a perspective (2021) (37)
- Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires (2017) (36)
- Structure and Dynamics of the Liquid–Water/Zinc-Oxide Interface from Machine Learning Potential Simulations (2019) (36)
- An assessment of the structural resolution of various fingerprints commonly used in machine learning (2020) (32)
- Orbital-Dependent Electronic Friction Significantly Affects the Description of Reactive Scattering of N2 from Ru(0001) (2019) (31)
- High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium. (2018) (29)
- Roadmap on Machine learning in electronic structure (2022) (28)
- Self-Diffusion of Surface Defects at Copper–Water Interfaces (2017) (28)
- Spectral broadening due to long-range Coulomb interactions in the molecular metal TTF-TCNQ (2007) (27)
- Heterogeneous Crystallization of the Phase Change Material GeTe via Atomistic Simulations (2015) (26)
- Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance. (2020) (25)
- A Full-Dimensional Neural Network Potential-Energy Surface for Water Clusters up to the Hexamer (2013) (24)
- Neural Network Potentials: A Concise Overview of Methods. (2021) (24)
- Insights into Water Permeation through hBN Nanocapillaries by Ab Initio Machine Learning Molecular Dynamics Simulations. (2020) (22)
- Molecular composition of liquid sulfur. (2002) (21)
- A critical comparison of neural network potentials for molecular reaction dynamics with exact permutation symmetry. (2019) (21)
- Global optimization of copper clusters at the ZnO(101¯0) surface using a DFT-based neural network potential and genetic algorithms. (2020) (20)
- Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations. (2018) (20)
- Water structuring properties of carbohydrates, molecular dynamics studies on 1,5-anhydro-D-fructose (2001) (19)
- Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of MnxGey compounds (2020) (19)
- Properties of α-Brass Nanoparticles. 1. Neural Network Potential Energy Surface (2020) (19)
- Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels. (2020) (18)
- High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions (2021) (18)
- Maximally resolved anharmonic OH vibrational spectrum of the water/ZnO(101¯0) interface from a high-dimensional neural network potential. (2018) (16)
- Atomistic simulations of thermal conductivity in GeTe nanowires (2019) (16)
- Proton-Transfer-Driven Water Exchange Mechanism in the Na+ Solvation Shell. (2017) (15)
- Closing the gap between theory and experiment for lithium manganese oxide spinels using a high-dimensional neural network potential (2020) (14)
- Analysis of Energy Dissipation Channels in a Benchmark System of Activated Dissociation: N2 on Ru(0001) (2018) (13)
- Temperature effects on the ionic conductivity in concentrated alkaline electrolyte solutions. (2019) (13)
- Neural network potential-energy surfaces for atomistic simulations (2010) (13)
- Mode specific dynamics in the H2 + SH → H + H2S reaction. (2016) (13)
- Adsorption of Methanethiolate and Atomic Sulfur at the Cu(111) Surface: A Computational Study (2013) (13)
- Priming effects in the crystallization of the phase change compound GeTe from atomistic simulations. (2019) (12)
- Temperature dependence of the vibrational spectrum of porphycene: a qualitative failure of classical-nuclei molecular dynamics. (2019) (12)
- molecular dynamics for the simulation of infrared spectra † (2017) (12)
- Neural Network Potentials in Materials Modeling (2020) (11)
- Force-induced mechanical response of molecule-metal interfaces: molecular nanomechanics of propanethiolate self-assembled monolayers on Au(111). (2013) (11)
- Hybrid density functional theory benchmark study on lithium manganese oxides (2020) (10)
- Atomic mobility in the overheated amorphous GeTe compound for phase change memories (2016) (9)
- Dissociation of oxygen molecules on the Al(111) surface (2004) (9)
- Coulomb parameters and photoemission for the molecular metal TTF-TCNQ (2006) (9)
- Density anomaly of water at negative pressures from first principles (2018) (8)
- Peeling by Nanomechanical Forces: A Route to Selective Creation of Surface Structures. (2015) (8)
- Insights into lithium manganese oxide-water interfaces using machine learning potentials. (2021) (7)
- Erratum: High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide [Phys. Rev. B 83 , 153101 (2011)] (2012) (6)
- Coupled Cluster Molecular Dynamics of Condensed Phase Systems Enabled by Machine Learning Potentials: Liquid Water Benchmark. (2022) (6)
- Erratum: "Perspective: Machine learning potentials for atomistic simulations" [J. Chem. Phys. 145, 170901 (2016)]. (2016) (6)
- Behler, Reuter, and Scheffler Reply (2006) (6)
- Mechanism of amorphous phase stabilization in ultrathin films of monoatomic phase change material. (2021) (6)
- An experimentally validated neural-network potential energy surface for H-atom on free-standing graphene in full dimensionality. (2020) (6)
- New Insights into the Catalytic Activity of Cobalt Orthophosphate Co3(PO4)2 from Charge Density Analysis (2019) (4)
- High-Dimensional Neural Network Potentials for Complex Systems (2019) (4)
- Breakdown of Stokes–Einstein relation in the supercooled liquid state of phase change materials [Phys. Status Solidi B 249, No. 10, 1880–1885 (2012)] (2013) (4)
- A flexible and adaptive grid algorithm for global optimization utilizing basin hopping Monte Carlo. (2020) (3)
- Properties of α-Brass Nanoparticles II: Structure and Composition (2021) (3)
- Properties of $\alpha$-Brass Nanoparticles I: Neural Network Potential Energy Surface (2020) (3)
- High-Dimensional Neural Network Potentials for Atomistic Simulations (2019) (2)
- A criticial view on e$_g$ occupancy as a descriptor for oxygen evolution catalytic activity in LiMn$_2$O$_4$ nanoparticles (2020) (2)
- Atomic mobility and fast crystallization of the phase change compound GeTe (2013) (1)
- Coarse Grained Electronic Structure Using Neural Networks (2009) (1)
- High Order Path Integrals Made Easy: A Precise Assessment of Nuclear Quantum Effects in Liquid Water and its Isotopomers (2016) (1)
- Surface phase diagram prediction from a minimal number of DFT calculations: redox-active adsorbates on zinc oxide. (2017) (1)
- A bin and hash method for analyzing reference data and descriptors in machine learning potentials (2020) (1)
- A Hessian-based assessment of atomic forces for training machine learning interatomic potentials. (2021) (1)
- Properties of {\alpha}-Brass Nanoparticles II: Structure and Composition (2021) (0)
- Front Cover: Neural network potentials for metals and oxides – First applications to copper clusters at zinc oxide (Phys. Status Solidi B 6/2013) (2013) (0)
- DMol 3 A Standard Tool for Density-Functional Calculations (2003) (0)
- O ct 2 01 1 Microscopic origins of the anomalous melting behaviour of high-pressure sodium (2011) (0)
- 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) (2019) (0)
- High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark (2022) (0)
- Large scale molecular dynamics simulations of phase change materials (2012) (0)
- Machine-Learning-Potenziale (2016) (0)
- Fragility, dynamical heterogeneity, crystallization kinetics and structural relaxation of the phase change compound GeTe from large scale molecular dynamics simulations (2014) (0)
- Recent and forthcoming publications in pss: Phys. Status Solidi RRL 1–2/2013 (2013) (0)
- O 36 . 2 Tue 18 : 30 P 4 Oxidation of Ruthenium Surfaces — ∙ (2011) (0)
- Modern developments in multiphysics materials simulations III Time : Friday 10 : 15 – 13 : 00 Location : A 053 SYMS (2008) (0)
- Phase diagram of silicon using a DFT-based neural network potential (2008) (0)
- Structure Determination of Small Metal Clusters by Density-Functional Theory and Comparison with Experimental Far-Infrared Spectra (2005) (0)
- Construction of a Neural Network Potential for Supported Copper Clusters on Zinc Oxide Surfaces (2019) (0)
- Basis set limit and systematic errors in local-orbital based all-electron DFT (2006) (0)
- Directed phonon engineering in nanostructured Mn-Ge superlattices: Towards a description of heat transport in device-like structures (2014) (0)
- Large Scale molecular dynamics simulations of the crystallization of GeTe at the crystal-amorphous interface (2012) (0)
- Institute for Advanced Simulation Coarse Grained Electronic Structure Using Neural Networks (2009) (0)
- Correction: A critical comparison of neural network potentials for molecular reaction dynamics with exact permutation symmetry. (2020) (0)
- Develop and Verify Shot Peening Processes (2023) (0)
- Large Scale Molecular Dynamics Simulations of the Crystallization Dynamics of Amorphous and Liquid GeTe (2013) (0)
- Simulation of phase change materials for data storage (2013) (0)
- Ab initio quality study of the graphite-diamond phase coexistence (2010) (0)
- Vibrational dynamics of molecules at interfaces studied with laser-induced infrared fluorescence spectroscopy 10 : 30 (2017) (0)
- Size , charge , and isomer specific vibrational spectroscopy of metal clusters (2005) (0)
- Nanosession: Phase Change Materials (2013) (0)
- Data-driven many-body representations with chemical accuracy for molecular simulations from the gas to the condensed phase (2018) (0)
- Machine learning transferable atomic forces for large systems from underconverged molecular fragments (2023) (0)
- Ju l 2 01 2 Breakdown of Stokes-Einstein relation in the supercooled liquid state of phase change materials (2012) (0)
- Metal Substrates : Adsorption of O and / or H Time : Thursday 15 : 00 – (2008) (0)
- Next generation interatomic potentials for condensed systems (2014) (0)
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What Schools Are Affiliated With Jörg Behler?
Jörg Behler is affiliated with the following schools:
