The origin of complex behavior of liquid carbon: an insight from computer simulation

TL;DR

This paper uses molecular dynamics simulations with a machine-learning potential to explore the complex behavior of liquid carbon, revealing a low critical temperature that influences experimental melting point measurements.

Contribution

It introduces a machine-learning based simulation approach to study liquid carbon's behavior and identifies a low critical temperature affecting experimental results.

Findings

Liquid carbon exhibits a relatively low critical temperature.

Machine-learning potential GAP-20 effectively models liquid carbon.

The low critical temperature impacts the interpretation of graphite melting point experiments.

Abstract

In the present paper we perfomrm molecular dynamics simulation of liquid carbon with a machine-learning potential GAP-20. We show that within the framework of this model carbon demonstrates a relatively low critical temperature, which can affect the results of experimental measurements of melting point of graphite.