Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics

TL;DR

This study uses machine learning molecular dynamics to clarify the high-temperature phase diagram of silicon carbide, revealing its incongruent melting behavior and phase transitions, which resolves previous experimental conflicts.

Contribution

It introduces a Bayesian active learning workflow for accurate force field training and constructs the complete SiC phase diagram through large-scale MLMD simulations.

Findings

SiC melts incongruently via decomposition into silicon-rich and carbon phases

Carbon nanoclusters nucleate during cooling at high pressures

Reversible transition between decomposed mixture and homogeneous SiC liquid during heating

Abstract

Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent versus incongruent melting. Here, we employ large-scale machine learning molecular dynamics (MLMD) simulations to gain insights into SiC decomposition and phase transitions. Our approach relies on a Bayesian active learning workflow to efficiently train an accurate machine learning force field on density functional theory data. Our large-scale simulations provide direct indication that melting of SiC proceeds incongruently via decomposition into silicon-rich and carbon phases at high temperature and pressure. During cooling at high pressures, carbon nanoclusters nucleate and grow within the homogeneous molten liquid. During heating, the decomposed mixture reversibly transitions back into a homogeneous SiC liquid. The full pressure-temperature phase diagram of SiC is systematically constructed using MLMD simulations, providing new understanding of the nature of phases, resolving long-standing inconsistencies from previous experiments and yielding technologically relevant implications for processing and deposition of this material.