Abstract

The field of atomistic simulations of multicomponent materials and high entropy alloys is progressing rapidly, with challenging problems stimulating new creative solutions. In this Perspective, we present three topics that emerged very recently and that we anticipate will determine the future direction of research of high entropy alloys: the usage of machine-learning potentials for very accurate thermodynamics, the exploration of short-range order and its impact on macroscopic properties, and the more extensive exploitation of interstitial alloying and high entropy alloy surfaces for new technological applications. For each of these topics, we briefly summarize the key achievements, point out the aspects that still need to be addressed, and discuss possible future improvements and promising directions.

Highlights

  • High entropy alloys (HEAs),[1,2,3] more broadly referred to as compositionally complex alloys, are metallic mixtures of several elements in non-dilute concentrations

  • Other examples in the recent literature confirm the success of machine-learning potentials for the analysis of the hightemperature properties of HEAs: low rank potentials[49] were used to investigate the phase diagrams of bcc MoNbTaW and fcc CrFeCoNi with Monte Carlo simulations[50,51] and the formation of new structures was observed in both cases; a neural network potential was exploited to study a refractory high entropy melt;[52] and local lattice distortions and elastic constants were calculated at finite temperature with another moment tensor potentials (MTPs) for the fcc medium entropy alloy FeCoNi.[53]

  • Two new types of defects were considered: interstitial elements, which broaden the possibilities for materials design by considering off-lattice alloying and pave the way for efficient storage of gases, and surfaces, which extend the domain of application of HEAs to chemical reactions

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Summary

INTRODUCTION

High entropy alloys (HEAs),[1,2,3] more broadly referred to as compositionally complex alloys, are metallic mixtures of several elements in non-dilute concentrations. These three topics demonstrate how quickly the atomistic modeling of HEAs evolved from more general simulations toward very elaborate applications, which are difficult to explore from experiments alone, for example, chemical short-range order. They reveal how severe approximations in the early simulations can nowadays be overcome. High entropy ceramics were recently explored for the most disparate applications, as detailed in Ref. 22

Machine-learning potentials for HEAs and active learning
Application to thermodynamics
SHORT RANGE ORDER IMPACTS THE PROPERTIES OF HEAs
SRO and magnetism
SRO and mechanical properties
NEW DEFECTS IN HEAs
Interstitials
Surfaces
Findings
CONCLUSIONS AND OUTLOOK

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