Silicon liquid structure and crystal nucleation from ab-initio deep Metadynamics

Abstract

Studying the crystallization process of silicon is a challenging task since empirical potentials are not able to reproduce well the properties of both 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 drive efficiently 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 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 DFT 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.

References in corpus (14)

• Canonical sampling through velocity-rescaling
• Separable Dual Space Gaussian Pseudo-potentials
• Escaping free-energy minima
• Relativistic separable dual-space Gaussian Pseudopotentials from H to Rn
• PLUMED 2: New feathers for an old bird
• Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• Crystal Nucleation in Liquids: Open Questions and Future Challenges in Molecular Dynamics Simulations
• Libxc: a library of exchange and correlation functionals for density functional theory
• Nucleation mechanism for the direct graphite-to-diamond phase transition
• Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment
• Nucleation and crystallization process of silicon using Stillinger-Weber potential

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