Exploring mechanical and thermal properties of high-entropy ceramics via general machine learning potentials

TL;DR

This paper demonstrates the use of a machine learning potential to efficiently predict mechanical and thermal properties of high-entropy ceramics, significantly accelerating their development process.

Contribution

It introduces a general neuroevolution potential trained on unary and binary carbides, enabling accurate MD simulations across diverse high-entropy ceramic compositions.

Findings

NEP-based MD results agree with first-principles and experiments

The approach effectively predicts properties of various HECs

Accelerates high-entropy ceramic development

Abstract

The mechanical and thermal performance of high-entropy ceramics are critical to their use in extreme conditions. However, the vast composition space of high-entropy ceramic significantly hinders their development with desired mechanical and thermal properties. Herein, taking high-entropy carbides (HECs) as the model, we show the efficiency and effectiveness of exploring the mechanical and thermal properties via machine-learning-potential-based molecular dynamics (MD). Specifically, a general neuroevolution potential (NEP) with broad compositional applicability for HECs of ten transition metal elements from group IIIB-VIB is efficiently constructed from the small dataset comprising unary and binary carbides with an equal amount of ergodic chemical compositions. Based on this well-established NEP, MD simulations on mechanical and thermal properties of different HECs have shown good agreement with the results of first-principles calculations and experimental measurements, validating the accuracy, generalization, and reliability of using the developed general NEP in investigating mechanical and thermal performance of HECs. Our work provides an efficient solution to accelerate the search for high-entropy ceramics with desirable mechanical and thermal properties.