Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab initio simulations
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab initio simulations
|
|
| Автор |
Chtchelkatchev, N. M.
Ryltsev, R. E. Magnitskaya, M. V. Gorbunov, S. M. Cherednichenko, K. A. Solozhenko, V. L. Brazhkin, V. V. |
|
| Тематика |
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 |
|
| Описание |
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).
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/ ). |
|
| Дата |
2024-04-05T16:30:39Z
2024-04-05T16:30:39Z 2023 |
|
| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/submittedVersion |
|
| Идентификатор |
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 0021-9606 Final All Open Access, Green https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166783080&doi=10.1063%2f5.0165948&partnerID=40&md5=871032b40e5ce9d95a15cf60bdf0bfae https://arxiv.org/pdf/2305.06981 http://elar.urfu.ru/handle/10995/130699 10.1063/5.0165948 85166783080 001044514400020 |
|
| Язык |
en
|
|
| Связанные ресурсы |
info:eu-repo/grantAgreement/RSF//22-22-00806
|
|
| Права |
Open access (info:eu-repo/semantics/openAccess)
|
|
| Формат |
application/pdf
|
|
| Издатель |
American Institute of Physics Inc.
|
|
| Источник |
The Journal of Chemical Physics
Journal of Chemical Physics |
|