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

TL;DR

This paper develops a deep neural network potential trained on metadynamics data to accurately simulate silicon crystallization, capturing nucleation mechanisms and thermodynamics with DFT-level precision.

Contribution

It introduces a new collective variable based on the Debye structure factor and demonstrates an effective approach to model silicon nucleation with ab-initio accuracy.

Findings

Accurate free energy surface for silicon crystallization

Good agreement with experimental thermodynamic data

Insights into early nucleation stages

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.