Machine-learning Driven Synthesis of TiZrNbHfTaC5 High-Entropy Carbide

TL;DR

This paper presents a machine learning and theoretical approach to controllably synthesize high-entropy carbides, specifically TiZrNbHfTaC5, by predicting optimal temperature conditions for single-phase formation using simulations and experiments.

Contribution

It introduces a combined theoretical and experimental method using machine learning to determine synthesis conditions for high-entropy carbides, enabling targeted material fabrication.

Findings

Single-phase TiZrNbHfTaC5 forms at 2000 K.

Below 1200 K, the material decomposes into multiple phases.

The approach predicts formation conditions aligning with experimental results.

Abstract

Synthesis of high-entropy carbides (HEC) requires high temperatures that can be provided by electric arc plasma method. However, the formation temperature of a single-phase sample remains unknown. Moreover, under some temperatures multi-phase structures can emerge. In this work we developed an approach for a controllable synthesis of HEC TiZrNbHfTaC5 based on theoretical and experimental techniques. We used canonical Monte Carlo (CMC) simulations with the machine learning interatomic potentials to determine the temperature conditions for the formation of single-phase and multi-phase samples. In full agreement with the theory, the single-phase sample, produced with electric arc discharge, was observed at 2000 K. Below 1200 K the sample decomposed into (Ti-Nb-Ta)C and a mixture of (Zr-Hf-Ta)C, (Zr-Nb-Hf)C, (Zr-Nb)C, and (Zr-Ta)C. Our results demonstrate the conditions for the formation of HEC and we anticipate that our approach can pave the way towards targeted synthesis of multicomponent materials.