Behler, Jörg

[["dc.bibliographiccitation.issue","20"],["dc.bibliographiccitation.journal","Physical Review B"],["dc.bibliographiccitation.volume","101"],["dc.contributor.author","Eckhoff, Marco"],["dc.contributor.author","Blöchl, Peter E."],["dc.contributor.author","Behler, Jörg"],["dc.date.accessioned","2020-12-10T18:20:23Z"],["dc.date.available","2020-12-10T18:20:23Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1103/PhysRevB.101.205113"],["dc.identifier.eissn","2469-9969"],["dc.identifier.issn","2469-9950"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/75546"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation","SFB 1073: Kontrolle von Energiewandlung auf atomaren Skalen"],["dc.relation","SFB 1073 | Topical Area B | B03 Relaxation, Thermalisierung, Transport und Kondensation in hochangeregten Festkörpern"],["dc.relation","SFB 1073 | Topical Area C | C03 Vom Elektronentransfer zur chemischen Energiespeicherung: ab-initio Untersuchungen korrelierter Prozesse"],["dc.title","Hybrid density functional theory benchmark study on lithium manganese oxides"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","7363"],["dc.bibliographiccitation.issue","17"],["dc.bibliographiccitation.journal","The Journal of Physical Chemistry Letters"],["dc.bibliographiccitation.lastpage","7370"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Ghorbanfekr, Hossein"],["dc.contributor.author","Behler, Jörg"],["dc.contributor.author","Peeters, François M."],["dc.date.accessioned","2021-04-14T08:23:15Z"],["dc.date.available","2021-04-14T08:23:15Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1021/acs.jpclett.0c01739"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/80846"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1948-7185"],["dc.relation.issn","1948-7185"],["dc.title","Insights into Water Permeation through hBN Nanocapillaries by Ab Initio Machine Learning Molecular Dynamics Simulations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","054001"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Journal of Physics D: Applied Physics"],["dc.bibliographiccitation.volume","53"],["dc.contributor.author","Bosoni, E"],["dc.contributor.author","Campi, D"],["dc.contributor.author","Donadio, D"],["dc.contributor.author","Sosso, G C"],["dc.contributor.author","Behler, J"],["dc.contributor.author","Bernasconi, M"],["dc.date.accessioned","2021-04-14T08:27:32Z"],["dc.date.available","2021-04-14T08:27:32Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1088/1361-6463/ab5478"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82324"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1361-6463"],["dc.relation.issn","0022-3727"],["dc.title","Atomistic simulations of thermal conductivity in GeTe nanowires"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","142"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","The European Physical Journal B"],["dc.bibliographiccitation.volume","94"],["dc.contributor.author","Behler, Jörg"],["dc.contributor.author","Csányi, Gábor"],["dc.date.accessioned","2021-10-01T09:58:08Z"],["dc.date.available","2021-10-01T09:58:08Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract In the past two and a half decades machine learning potentials have evolved from a special purpose solution to a broadly applicable tool for large-scale atomistic simulations. By combining the efficiency of empirical potentials and force fields with an accuracy close to first-principles calculations they now enable computer simulations of a wide range of molecules and materials. In this perspective, we summarize the present status of these new types of models for extended systems, which are increasingly used for materials modelling. There are several approaches, but they all have in common that they exploit the locality of atomic properties in some form. Long-range interactions, most prominently electrostatic interactions, can also be included even for systems in which non-local charge transfer leads to an electronic structure that depends globally on all atomic positions. Remaining challenges and limitations of current approaches are discussed. Graphic Abstract"],["dc.description.abstract","Abstract In the past two and a half decades machine learning potentials have evolved from a special purpose solution to a broadly applicable tool for large-scale atomistic simulations. By combining the efficiency of empirical potentials and force fields with an accuracy close to first-principles calculations they now enable computer simulations of a wide range of molecules and materials. In this perspective, we summarize the present status of these new types of models for extended systems, which are increasingly used for materials modelling. There are several approaches, but they all have in common that they exploit the locality of atomic properties in some form. Long-range interactions, most prominently electrostatic interactions, can also be included even for systems in which non-local charge transfer leads to an electronic structure that depends globally on all atomic positions. Remaining challenges and limitations of current approaches are discussed. Graphic Abstract"],["dc.identifier.doi","10.1140/epjb/s10051-021-00156-1"],["dc.identifier.pii","156"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89995"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-469"],["dc.relation.eissn","1434-6036"],["dc.relation.issn","1434-6028"],["dc.title","Machine learning potentials for extended systems: a perspective"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","88"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of Chemical Theory and Computation"],["dc.bibliographiccitation.lastpage","99"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Schran, Christoph"],["dc.contributor.author","Behler, Jörg"],["dc.contributor.author","Marx, Dominik"],["dc.date.accessioned","2020-12-10T15:22:41Z"],["dc.date.available","2020-12-10T15:22:41Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1021/acs.jctc.9b00805"],["dc.identifier.eissn","1549-9626"],["dc.identifier.issn","1549-9618"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73494"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1232"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Chemical Science"],["dc.bibliographiccitation.lastpage","1243"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Hellström, Matti"],["dc.contributor.author","Quaranta, Vanessa"],["dc.contributor.author","Behler, Jörg"],["dc.date.accessioned","2021-06-01T10:50:50Z"],["dc.date.available","2021-06-01T10:50:50Z"],["dc.date.issued","2019"],["dc.description.abstract","Neural network molecular dynamics simulations unravel the long-range proton transport properties of ZnO–water interfaces."],["dc.description.abstract","Long-range charge transport is important for many applications like batteries, fuel cells, sensors, and catalysis. Obtaining microscopic insights into the atomistic mechanism is challenging, in particular if the underlying processes involve protons as the charge carriers. Here, large-scale reactive molecular dynamics simulations employing an efficient density-functional-theory-based neural network potential are used to unravel long-range proton transport mechanisms at solid–liquid interfaces, using the zinc oxide–water interface as a prototypical case. We find that the two most frequently occurring ZnO surface facets, (101̄0) and (112̄0), that typically dominate the morphologies of zinc oxide nanowires and nanoparticles, show markedly different proton conduction behaviors along the surface with respect to the number of possible proton transfer mechanisms, the role of the solvent for long-range proton migration, as well as the proton transport dimensionality. Understanding such surface-facet-specific mechanisms is crucial for an informed bottom-up approach for the functionalization and application of advanced oxide materials."],["dc.identifier.doi","10.1039/C8SC03033B"],["dc.identifier.eissn","2041-6539"],["dc.identifier.issn","2041-6520"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/86804"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation.eissn","2041-6539"],["dc.relation.issn","2041-6520"],["dc.title","One-dimensional vs. two-dimensional proton transport processes at solid–liquid zinc-oxide–water interfaces"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","526"],["dc.bibliographiccitation.journal","Faraday Discussions"],["dc.bibliographiccitation.lastpage","546"],["dc.bibliographiccitation.volume","221"],["dc.contributor.author","Litman, Yair"],["dc.contributor.author","Behler, Jörg"],["dc.contributor.author","Rossi, Mariana"],["dc.date.accessioned","2021-04-14T08:27:48Z"],["dc.date.available","2021-04-14T08:27:48Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1039/c9fd00056a"],["dc.identifier.eissn","1364-5498"],["dc.identifier.issn","1359-6640"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82406"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1364-5498"],["dc.relation.issn","1359-6640"],["dc.title","Temperature dependence of the vibrational spectrum of porphycene: a qualitative failure of classical-nuclei molecular dynamics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","244901"],["dc.bibliographiccitation.issue","24"],["dc.bibliographiccitation.journal","Journal of Applied Physics"],["dc.bibliographiccitation.volume","127"],["dc.contributor.author","Mangold, Claudia"],["dc.contributor.author","Chen, Shunda"],["dc.contributor.author","Barbalinardo, Giuseppe"],["dc.contributor.author","Behler, Jörg"],["dc.contributor.author","Pochet, Pascal"],["dc.contributor.author","Termentzidis, Konstantinos"],["dc.contributor.author","Han, Yang"],["dc.contributor.author","Chaput, Laurent"],["dc.contributor.author","Lacroix, David"],["dc.contributor.author","Donadio, Davide"],["dc.date.accessioned","2021-04-14T08:25:38Z"],["dc.date.available","2021-04-14T08:25:38Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1063/5.0009550"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81693"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1089-7550"],["dc.relation.issn","0021-8979"],["dc.title","Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of Mn x Ge y compounds"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","5737"],["dc.bibliographiccitation.issue","28"],["dc.bibliographiccitation.journal","The Journal of Physical Chemistry A"],["dc.bibliographiccitation.lastpage","5745"],["dc.bibliographiccitation.volume","124"],["dc.contributor.author","Lu, Dandan"],["dc.contributor.author","Behler, Jörg"],["dc.contributor.author","Li, Jun"],["dc.date.accessioned","2021-04-14T08:24:31Z"],["dc.date.available","2021-04-14T08:24:31Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1021/acs.jpca.0c04182"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81315"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1520-5215"],["dc.relation.issn","1089-5639"],["dc.title","Accurate Global Potential Energy Surfaces for the H + CH3OH Reaction by Neural Network Fitting with Permutation Invariance"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","15786"],["dc.bibliographiccitation.issue","69"],["dc.bibliographiccitation.journal","Chemistry – A European Journal"],["dc.bibliographiccitation.lastpage","15794"],["dc.bibliographiccitation.volume","25"],["dc.contributor.author","Keil, Helena"],["dc.contributor.author","Hellström, Matti"],["dc.contributor.author","Stückl, Claudia"],["dc.contributor.author","Herbst‐Irmer, Regine"],["dc.contributor.author","Behler, Jörg"],["dc.contributor.author","Stalke, Dietmar"],["dc.date.accessioned","2020-12-10T14:05:54Z"],["dc.date.available","2020-12-10T14:05:54Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1002/chem.v25.69"],["dc.identifier.eissn","1521-3765"],["dc.identifier.issn","0947-6539"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69700"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","New Insights into the Catalytic Activity of Cobalt Orthophosphate Co 3 (PO 4 ) 2 from Charge Density Analysis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]