Computational tuning of the elastic properties of low- and high-entropy ultra-high temperature ceramics

TL;DR

This study uses machine learning to tune the elastic properties of ultra-high temperature ceramics, revealing how mixture composition affects their mechanical behavior and identifying promising new compounds for extreme environment applications.

Contribution

The paper introduces a machine learning model that predicts elastic properties of UHTCs, demonstrating property deviations in both low- and high-entropy regimes and proposing new candidate compositions.

Findings

Elastic constants deviate from rule of mixtures in both entropy limits.

Lattice mismatch causes distortion, enabling property tuning.

Identified HfCVCZrC as a promising mixture with adjustable Young's modulus.

Abstract

Ultra-high temperature ceramics (UHTCs) represent a class of crystalline materials for extreme environments. They can withstand extremely high temperatures but are mechanically difficult to work with due to their inherent brittleness. Mixture compounds, in particular high-entropy mixtures, offer a pathway to tune the physical properties of UHTCs such as their elastic constants. Here we fine-tune the MACE-MPA-0 universal machine-learning potential on rocksalt carbide UHTCs containing group IV-V metals and demonstrate that not only do the elastic constants deviate from the rule of mixtures approximation in the high-entropy limit, but also in the low-entropy limit of binary and ternary mixtures. We find that this is caused by distortion imposed by the lattice mismatch, enabling the tuning of the physical properties of UHTC mixtures in both low- and high-entropy compounds. We identify a three-component mixture compound, HfCVCZrC, as the best balance between synthesizability and toughness, and apply our developed MACE-UHTC model to identify a range of non-equimolar candidate compositions of this compound which may enable the synthesis of a mixture UHTC with a Young's modulus up to 40 GPa below that of ZrC.