Machine-learned interatomic potentials by active learning:amorphous and liquid hafnium dioxide

查看全文 作　　者：Ganesh [1]Sivaraman;Anand Narayanan [2,3]Krishnamoorthy;Matthias [2]Baur;Christian [2]Holm;Marius [4]Stan;Gábor [5]Csányi;Chris [6]Benmore;Álvaro Vázquez-[7]Mayagoitia

机构地区：[1]Leadership Computing Facility,Argonne National Laboratory,Argonne,IL 60439,USA;[2]Institute for Computational Physics,Universität Stuttgart,Allmandring 3,Stuttgart 70569,Germany;[3]Helmholtz-Institute Münster:Ionics in Energy Storage(IEK-12),Forschungszentrum Jülich GmbH,Corrensstrasse 46,Münster 48149,Germany;[4]Applied Materials Division,Argonne National Laboratory,Argonne,IL 60439,USA;[5]Department of Engineering,University of Cambridge,Trumpington Street,Cambridge CB21PZ,UK;[6]X-ray Science Division,Argonne National Laboratory,Argonne,IL 60439,USA;[7]Computational Science Division,Argonne National Laboratory,Argonne,IL 60439,USA

出　　处：《npj Computational Materials》索引2020年第1期,共8页

基　　金：This material is based upon work supported by Laboratory Directed Research and Development funding from Argonne National Laboratory,provided by the Director,Office of Science,of the U.S.Department of Energy(DOE)under Contract No.DEAC02-06CH11357;This research used resources of the Argonne Leadership Computing Facility,which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357;Argonne National Laboratory’s work was supported by the U.S.DOE,Office of Science,under contract DE-AC02-06CH11357;This research used resources of the Advanced Photon Source,a U.S.DOE Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No.DE-AC02-06CH11357;Use of the Center for Nanoscale Materials,an Office of Science user facility,was supported by the U.S.DOE,Office of Science,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357;C.H.and A.N.K gratefully acknowledges useful discussions with Dr.Jens Smiatek,Dr.Frank Uhlig,and financial support from the German Funding Agency(Deutsche Forschungsgemeinschaft-DFG)under Germany’s Excellence Strategy—EXC 2075—390740016. 摘　　要：We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential(GAP).Our active learning scheme consists of an unsupervised machine learning(ML)scheme coupled with a Bayesian optimization technique that evaluates the GAP model.We apply this scheme to a Hafnium dioxide(HfO2)dataset generated from a“melt-quench”ab initio molecular dynamics(AIMD)protocol.Our results show that the active learning scheme,with no prior knowledge of the dataset,is able to extract a configuration that reaches the required energy fit tolerance.Further,molecular dynamics(MD)simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO2 with near ab initio precision and quench rates(i.e.,1.0 K/ps)not accessible via AIMD.The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment.In addition,the calculated diffusion constants are in good agreement with previous ab initio studies. 关键词：AMORPHOUS dioxide SCHEME

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Machine-learned interatomic potentials by active learning:amorphous and liquid hafnium dioxide

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