Specific Heat Anomalies and Local Symmetry Breaking in (Anti-)Fluorite Materials: A Machine Learning Molecular Dynamics Study

TL;DR

This study uses machine learning molecular dynamics to analyze specific heat anomalies and local symmetry breaking in (anti-)fluorite materials, revealing the nature of defect structures and phase transitions at high temperatures.

Contribution

It introduces a local order parameter to characterize specific heat anomalies and distinguishes defect types in fluorite and anti-fluorite structures using MLMD simulations.

Findings

MLMD accurately reproduces thermal properties of thorium dioxide and lithium oxide.

The local order parameter effectively identifies defect structures associated with heat anomalies.

Liquid-like structures dominate above the transition temperature, indicating symmetry breaking.

Abstract

Understanding the high-temperature properties of materials with (anti-)fluorite structures is crucial for their application in nuclear reactors. In this study, we employ machine learning molecular dynamics (MLMD) simulations to investigate the high-temperature thermal properties of thorium dioxide, which has a fluorite structure, and lithium oxide, which has an anti-fluorite structure. Our results show that MLMD simulations effectively reproduce the reported thermal properties of these materials. A central focus of this work is the analysis of specific heat anomalies in these materials at high temperatures, commonly referred to as Bredig, pre-melting, or $\lambda$-transitions. We demonstrate that a local order parameter, analogous to those used to describe liquid-liquid transitions in supercooled water and liquid silica, can effectively characterize these specific heat anomalies. The local order parameter identifies two distinct types of defective structures: lattice defect-like and liquid-like local structures. Above the transition temperature, liquid-like local structures predominate, and the sub-lattice character of mobile atoms disappears.