Please use this identifier to cite or link to this item: http://elar.urfu.ru/handle/10995/130699
Title: Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab initio simulations
Authors: Chtchelkatchev, N. M.
Ryltsev, R. E.
Magnitskaya, M. V.
Gorbunov, S. M.
Cherednichenko, K. A.
Solozhenko, V. L.
Brazhkin, V. V.
Issue Date: 2023
Publisher: American Institute of Physics Inc.
Citation: Chtchelkatchev, 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/10.1063/5.0165948
Chtchelkatchev, 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/10.1063/5.0165948
Abstract: Boron 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).
Keywords: CHEMICAL ELEMENTS
CRYSTAL STRUCTURE
III-V SEMICONDUCTORS
MELTING
MOLECULAR DYNAMICS
THERMODYNAMICS
AB INITIO SIMULATIONS
BORON PHOSPHIDE
COVALENT MATERIALS
HIGH PRESSURE
LEARNING POTENTIAL
LOCAL MELTING
LOCAL STRUCTURE
LOW PRESSURES
MACHINE-LEARNING
SUBNETWORKS
DEEP LEARNING
URI: http://elar.urfu.ru/handle/10995/130699
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85166783080
WOS ID: 001044514400020
PURE ID: 43268282
ISSN: 0021-9606
DOI: 10.1063/5.0165948
Sponsorship: Russian Science Foundation, RSF: 2019A1054, 22-22-00806
V.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/ ).
RSCF project card: 22-22-00806
Appears in Collections:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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