Behler, Jörg

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[["dc.bibliographiccitation.firstpage","1110"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Proceedings of the National Academy of Sciences of the United States of America"],["dc.bibliographiccitation.lastpage","1115"],["dc.bibliographiccitation.volume","116"],["dc.contributor.author","Cheng, Bingqing"],["dc.contributor.author","Engel, Edgar A."],["dc.contributor.author","Behler, Jörg"],["dc.contributor.author","Dellago, Christoph"],["dc.contributor.author","Ceriotti, Michele"],["dc.date.accessioned","2019-08-01T13:34:10Z"],["dc.date.available","2019-08-01T13:34:10Z"],["dc.date.issued","2019"],["dc.description.abstract","Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations, and proton disorder. This is made possible by combining advanced free-energy methods and state-of-the-art machine-learning techniques. The ab initio description leads to structural properties in excellent agreement with experiments and reliable estimates of the melting points of light and heavy water. We observe that nuclear-quantum effects contribute a crucial [Formula: see text] to the stability of ice Ih, making it more stable than ice Ic. Our computational approach is general and transferable, providing a comprehensive framework for quantitative predictions of ab initio thermodynamic properties using machine-learning potentials as an intermediate step."],["dc.identifier.arxiv","1811.08630v1"],["dc.identifier.doi","10.1073/pnas.1815117116"],["dc.identifier.pmid","30610171"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62255"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.eissn","1091-6490"],["dc.relation.issn","0027-8424"],["dc.relation.issn","1091-6490"],["dc.title","Ab initio thermodynamics of liquid and solid water"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

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[["dc.bibliographiccitation.firstpage","241725"],["dc.bibliographiccitation.issue","24"],["dc.bibliographiccitation.journal","The Journal of Chemical Physics"],["dc.bibliographiccitation.volume","148"],["dc.contributor.author","Nguyen, Thuong T."],["dc.contributor.author","Székely, Eszter"],["dc.contributor.author","Imbalzano, Giulio"],["dc.contributor.author","Behler, Jörg"],["dc.contributor.author","Csányi, Gábor"],["dc.contributor.author","Ceriotti, Michele"],["dc.contributor.author","Götz, Andreas W."],["dc.contributor.author","Paesani, Francesco"],["dc.date.accessioned","2020-12-10T18:12:39Z"],["dc.date.available","2020-12-10T18:12:39Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1063/1.5024577"],["dc.identifier.eissn","1089-7690"],["dc.identifier.issn","0021-9606"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/74450"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]