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dc.contributor.authorChtchelkatchev, N. M.en
dc.contributor.authorRyltsev, R. E.en
dc.contributor.authorMagnitskaya, M. V.en
dc.contributor.authorGorbunov, S. M.en
dc.contributor.authorCherednichenko, K. A.en
dc.contributor.authorSolozhenko, V. L.en
dc.contributor.authorBrazhkin, V. V.en
dc.date.accessioned2024-04-05T16:30:39Z-
dc.date.available2024-04-05T16:30:39Z-
dc.date.issued2023-
dc.identifier.citationChtchelkatchev, NM, Ryltsev, RE, Magnitskaya, MV, Gorbunov, SM, Cherednichenko, KA, Solozhenko, VL & Brazhkin, VV 2023, 'Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab initio simulations', Journal of Chemical Physics, Том. 159, № 6, 064507. https://doi.org/10.1063/5.0165948harvard_pure
dc.identifier.citationChtchelkatchev, N. M., Ryltsev, R. E., Magnitskaya, M. V., Gorbunov, S. M., Cherednichenko, K. A., Solozhenko, V. L., & Brazhkin, V. V. (2023). Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab initio simulations. Journal of Chemical Physics, 159(6), [064507]. https://doi.org/10.1063/5.0165948apa_pure
dc.identifier.issn0021-9606-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access, Green3
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85166783080&doi=10.1063%2f5.0165948&partnerID=40&md5=871032b40e5ce9d95a15cf60bdf0bfae1
dc.identifier.otherhttps://arxiv.org/pdf/2305.06981pdf
dc.identifier.urihttp://elar.urfu.ru/handle/10995/130699-
dc.description.abstractBoron phosphide (BP) is a (super)hard semiconductor constituted of light elements, which is promising for high demand applications at extreme conditions. The behavior of BP at high temperatures and pressures is of special interest but is also poorly understood because both experimental and conventional ab initio methods are restricted to studying refractory covalent materials. The use of machine learning interatomic potentials is a revolutionary trend that gives a unique opportunity for high-temperature study of materials with ab initio accuracy. We develop a deep machine learning potential (DP) for accurate atomistic simulations of the solid and liquid phases of BP as well as their transformations near the melting line. Our DP provides quantitative agreement with experimental and ab initio molecular dynamics data for structural and dynamic properties. DP-based simulations reveal that at ambient pressure, a tetrahedrally bonded cubic BP crystal melts into an open structure consisting of two interpenetrating sub-networks of boron and phosphorous with different structures. Structure transformations of BP melt under compressing are reflected by the evolution of low-pressure tetrahedral coordination to high-pressure octahedral coordination. The main contributions to structural changes at low pressures are made by the evolution of medium-range order in the B-subnetwork and, at high pressures, by the change of short-range order in the P-subnetwork. Such transformations exhibit an anomalous behavior of structural characteristics in the range of 12-15 GPa. DP-based simulations reveal that the Tm(P) curve develops a maximum at P ≈ 13 GPa, whereas experimental studies provide two separate branches of the melting curve, which demonstrate the opposite behavior. Analysis of the results obtained raises open issues in developing machine learning potentials for covalent materials and stimulates further experimental and theoretical studies of melting behavior in BP. © 2023 Author(s).en
dc.description.sponsorshipRussian Science Foundation, RSF: 2019A1054, 22-22-00806en
dc.description.sponsorshipV.L.S. and K.A.C. are thankful to Dr. Saori I. Kawaguchi for assistance in laser-heated DAC experiments at the BL10XU beamline. This work was supported by the Russian Science Foundation under Grant RSF No. 22-22-00806 ( https://rscf.ru/en/project/22-22-00806/ ). Synchrotron X-ray diffraction studies have been performed during beamtime allocated to proposal Grant No. 2019A1054 at SPring-8. Numerical calculations were performed using computing resources of the federal collective usage center Complex for Simulation and Data Processing for Mega-science Facilities at the NRC “Kurchatov Institute” ( http://ckp.nrcki.ru/ ), supercomputers at the Joint Supercomputer Center of RAS (JSCC RAS), and the “Uran” cluster of IMM UB RAS ( https://parallel.uran.ru/ ).en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherAmerican Institute of Physics Inc.en
dc.relationinfo:eu-repo/grantAgreement/RSF//22-22-00806en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceThe Journal of Chemical Physics2
dc.sourceJournal of Chemical Physicsen
dc.subjectCHEMICAL ELEMENTSen
dc.subjectCRYSTAL STRUCTUREen
dc.subjectIII-V SEMICONDUCTORSen
dc.subjectMELTINGen
dc.subjectMOLECULAR DYNAMICSen
dc.subjectTHERMODYNAMICSen
dc.subjectAB INITIO SIMULATIONSen
dc.subjectBORON PHOSPHIDEen
dc.subjectCOVALENT MATERIALSen
dc.subjectHIGH PRESSUREen
dc.subjectLEARNING POTENTIALen
dc.subjectLOCAL MELTINGen
dc.subjectLOCAL STRUCTUREen
dc.subjectLOW PRESSURESen
dc.subjectMACHINE-LEARNINGen
dc.subjectSUBNETWORKSen
dc.subjectDEEP LEARNINGen
dc.titleLocal structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab initio simulationsen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.type|info:eu-repo/semantics/submittedVersionen
dc.identifier.doi10.1063/5.0165948-
dc.identifier.scopus85166783080-
local.contributor.employeeChtchelkatchev, N.M., Vereshchagin Institute for High Pressure Physics, Russian Academy of Sciences, Troitsk, Moscow, 108840, Russian Federationen
local.contributor.employeeRyltsev, R.E., Institute of Metallurgy, the Ural Branch, the Russian Academy of Sciences, Ekaterinburg, 620016, Russian Federation, Ural Federal University, Ekaterinburg, 620002, Russian Federationen
local.contributor.employeeMagnitskaya, M.V., Vereshchagin Institute for High Pressure Physics, Russian Academy of Sciences, Troitsk, Moscow, 108840, Russian Federationen
local.contributor.employeeGorbunov, S.M., Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russian Federationen
local.contributor.employeeCherednichenko, K.A., LSPM-CNRS, Universite Sorbonne Paris Nord, Villetaneuse, Franceen
local.contributor.employeeSolozhenko, V.L., LSPM-CNRS, Universite Sorbonne Paris Nord, Villetaneuse, Franceen
local.contributor.employeeBrazhkin, V.V., Vereshchagin Institute for High Pressure Physics, Russian Academy of Sciences, Troitsk, Moscow, 108840, Russian Federationen
local.issue6-
local.volume159-
dc.identifier.wos001044514400020-
local.contributor.departmentVereshchagin Institute for High Pressure Physics, Russian Academy of Sciences, Troitsk, Moscow, 108840, Russian Federationen
local.contributor.departmentInstitute of Metallurgy, the Ural Branch, the Russian Academy of Sciences, Ekaterinburg, 620016, Russian Federationen
local.contributor.departmentUral Federal University, Ekaterinburg, 620002, Russian Federationen
local.contributor.departmentMoscow Institute of Physics and Technology, Dolgoprudny, 141701, Russian Federationen
local.contributor.departmentLSPM-CNRS, Universite Sorbonne Paris Nord, Villetaneuse, Franceen
local.identifier.pure43268282-
local.description.order064507-
local.identifier.eid2-s2.0-85166783080-
local.fund.rsf22-22-00806-
local.identifier.wosWOS:001044514400020-
local.identifier.pmid37551816-
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