Show simple item record

2020-11-30Zeitschriftenartikel DOI: 10.3389/fchem.2020.601029
Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal–Oxide Interfaces
dc.contributor.authorLi, Xiaoke
dc.contributor.authorPaier, Wolfgang
dc.contributor.authorPaier, Joachim
dc.date.accessioned2021-04-09T09:04:47Z
dc.date.available2021-04-09T09:04:47Z
dc.date.issued2020-11-30none
dc.date.updated2020-12-14T06:28:42Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/23307
dc.description.abstractThe goal of many computational physicists and chemists is the ability to bridge the gap between atomistic length scales of about a few multiples of an Ångström (Å), i. e., 10−10 m, and meso- or macroscopic length scales by virtue of simulations. The same applies to timescales. Machine learning techniques appear to bring this goal into reach. This work applies the recently published on-the-fly machine-learned force field techniques using a variant of the Gaussian approximation potentials combined with Bayesian regression and molecular dynamics as efficiently implemented in the Vienna ab initio simulation package, VASP. The generation of these force fields follows active-learning schemes. We apply these force fields to simple oxides such as MgO and more complex reducible oxides such as iron oxide, examine their generalizability, and further increase complexity by studying water adsorption on these metal oxide surfaces. We successfully examined surface properties of pristine and reconstructed MgO and Fe3O4 surfaces. However, the accurate description of water–oxide interfaces by machine-learned force fields, especially for iron oxides, remains a field offering plenty of research opportunities.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectMgOeng
dc.subjectmagnetiteeng
dc.subjectdensity functional theoryeng
dc.subjectmachine learningeng
dc.subjectforce fieldseng
dc.subject.ddc540 Chemie und zugeordnete Wissenschaftennone
dc.titleMachine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal–Oxide Interfacesnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/23307-4
dc.identifier.doi10.3389/fchem.2020.601029none
dc.identifier.doihttp://dx.doi.org/10.18452/22699
dc.type.versionpublishedVersionnone
local.edoc.container-titleFrontiers in Chemistrynone
local.edoc.pages15none
local.edoc.anmerkungThis article was supported by the German Research Foundation (DFG) and the Open Access Publication Fund of Humboldt-Universität zu Berlin.none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionMathematisch-Naturwissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameFrontiers Medianone
local.edoc.container-publisher-placeLausannenone
local.edoc.container-volume8none
dc.description.versionPeer Reviewednone
local.edoc.container-articlenumber601029none
dc.identifier.eissn2296-2646
local.edoc.affiliationLi, Xiaoke; 1Institut für Chemie, Humboldt-Universität zu Berlin, Berlin, Germanynone
local.edoc.affiliationPaier, Wolfgang; 2Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute HHI, Berlin, Germanynone
local.edoc.affiliationPaier, Joachim; 1Institut für Chemie, Humboldt-Universität zu Berlin, Berlin, Germanynone

Show simple item record