Melting line of silicon modelled with a machine-learning potential

TL;DR

This study uses a machine learning potential to model silicon's phase diagram, accurately capturing the melting line's pressure dependence but underestimating melting temperatures and failing to predict high-pressure phases.

Contribution

It demonstrates the capabilities and limitations of the SNAP machine learning potential in modeling silicon's phase behavior.

Findings

Melting line of silicon is linear with pressure, matching experiments.

The model underestimates melting temperatures.

Fails to predict high-pressure silicon phases.

Abstract

In the present study we investigate the phase diagram of silicon within the framework of SNAP machine learning potential model. We show that the melting line of diamond phase of silicon is a linear function of pressure, which is in good agreement with experimental data. At the same time the melting temperature is strongly underestimated. Also, this model fails to predict the high pressure phases of silicon.