Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics

Pedro A. Santos-Florez, Howard Yanxon, Byungkyun Kang, Yansun Yao, and Qiang Zhu
Phys. Rev. Lett. 129, 185701 – Published 28 October 2022
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Abstract

In this Letter, we present a framework that combines machine learning potential (MLP) and metadynamics to investigate solid-solid phase transition. Based on the spectral descriptors and neural networks regression, we develop a scalable MLP model to warrant an accurate interpolation of the energy surface where two phases coexist. Applying it to the simulation of B4–B1 phase transition of GaN under 50 GPa with different model sizes, we observe sequential change of the phase transition mechanism from collective modes to nucleation and growths. When the size is at or below 128 000 atoms, the nucleation and growth appear to follow a preferred direction. At larger sizes, the nuclei occur at multiple sites simultaneously and grow to microstructures by passing the critical size. The observed change of the atomistic mechanism manifests the importance of statistical sampling with large system size in phase transition modeling.

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  • Received 1 June 2022
  • Revised 5 August 2022
  • Accepted 11 September 2022

DOI:https://doi.org/10.1103/PhysRevLett.129.185701

© 2022 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Pedro A. Santos-Florez1, Howard Yanxon2, Byungkyun Kang1, Yansun Yao3,*, and Qiang Zhu1,†

  • 1Department of Physics and Astronomy, University of Nevada, Las Vegas, Nevada 89154, USA
  • 2X-Ray Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
  • 3Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan, S7N 5E2, Canada

  • *yansun.yao@usask.ca
  • qiang.zhu@unlv.edu

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Issue

Vol. 129, Iss. 18 — 28 October 2022

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