Phase diagram of uranium from ab initio calculations and machine learning

Ivan A. Kruglov, Alexey Yanilkin, Artem R. Oganov, and Pavel Korotaev
Phys. Rev. B 100, 174104 – Published 12 November 2019
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Abstract

Experimental studies of materials at extreme conditions are challenging, and as a consequence, P-T phase diagrams are still unknown for many elements and materials. In this work, we present the P-T phase diagram of uranium calculated up to extreme conditions. First, we searched for possible crystal structures using the evolutionary algorithm USPEX. Their free energies were then calculated using thermodynamic integration (TI) and temperature-dependent effective potential techniques. TI was performed using molecular dynamics, employing a machine learning (ML) force field trained on energies and forces from density-functional calculations at the generalized gradient approximation level. The prediction error of the ML force field for the energy was less than 10 meV/atom. Using thermodynamic perturbation theory (including first and second order corrections), from the free energies of the ML force field, we obtained free energies and phase diagram at the level of quality of the underlying density-functional calculations at pressures up to 800 GPa and temperatures up to 16 000 K.

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  • Received 7 May 2019
  • Revised 17 October 2019

DOI:https://doi.org/10.1103/PhysRevB.100.174104

©2019 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Ivan A. Kruglov1,2,*, Alexey Yanilkin1,2, Artem R. Oganov3,2,1, and Pavel Korotaev1,4

  • 1Dukhov Research Institute of Automatics (VNIIA), Moscow 127055, Russian Federation
  • 2Moscow Institute of Physics and Technology, Dolgoprudny 141700, Russian Federation
  • 3Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Moscow 143026, Russian Federation
  • 4Material Modeling and Development Laboratory, NUST MISiS, Moscow 119991, Russian Federation

  • *Corresponding author: ivan.kruglov@phystech.edu

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Issue

Vol. 100, Iss. 17 — 1 November 2019

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