Silicon Liquid Structure and Crystal Nucleation from Ab Initio Deep Metadynamics

Luigi Bonati and Michele Parrinello
Phys. Rev. Lett. 121, 265701 – Published 26 December 2018
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Abstract

Studying the crystallization process of silicon is a challenging task since empirical potentials are not able to reproduce well the properties of both a semiconducting solid and metallic liquid. On the other hand, nucleation is a rare event that occurs in much longer timescales than those achievable by ab initio molecular dynamics. To address this problem, we train a deep neural network potential based on a set of data generated by metadynamics simulations using a classical potential. We show how this is an effective way to collect all the relevant data for the process of interest. In order to efficiently drive the crystallization process, we introduce a new collective variable based on the Debye structure factor. We are able to encode the long-range order information in a local variable which is better suited to describe the nucleation dynamics. The reference energies are then calculated using the strongly constrained and appropriately normed (SCAN) exchange-correlation functional, which is able to get a better description of the bonding complexity of the Si phase diagram. Finally, we recover the free energy surface with a density functional theory accuracy, and we compute the thermodynamics properties near the melting point, obtaining a good agreement with experimental data. In addition, we study the early stages of the crystallization process, unveiling features of the nucleation mechanism.

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  • Received 1 October 2018

DOI:https://doi.org/10.1103/PhysRevLett.121.265701

© 2018 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Luigi Bonati1,2 and Michele Parrinello2,3,*

  • 1Department of Physics, ETH Zurich, c/o Università della Svizzera italiana, Via Giuseppe Buffi 13, CH-6900, Lugano, Switzerland
  • 2Facoltà di Informatica, Instituto di Scienze Computazionali, National Center for Computational Design and Discovery of Novel Materials (MARVEL), Università della Svizzera italiana, Via Giuseppe Buffi 13, CH-6900, Lugano, Switzerland
  • 3Department of Chemistry and Applied Biosciences, ETH Zurich, c/o Università della Svizzera italiana, Via Giuseppe Buffi 13, CH-6900, Lugano, Switzerland

  • *parrinello@phys.chem.ethz.ch

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Vol. 121, Iss. 26 — 28 December 2018

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