Frontiers in atomistic simulations of high entropy alloys

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.

Bayesian Optimization of High‐Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction**

“…[17] However,a st he number of constituent elements increases,t he number of possible compositions grows combinatorially large and individual point testing cannot be accomplished within realistic time scales (see Modeling the catalytic activity of highly diverse and complex surfaces is still in its infancy with only af ew studies conducted, [1,3,4,20,21] and modeling of other aspects relevant for catalysis,such as surface stability under reaction conditions,is also being investigated. [22,23] We propose away to estimate the number of experiments needed using am odel that has been found to correctly predict experimental trends for electrocatalytic ORR across hundreds of different alloy compositions within the Ag-Ir-Pd-Pt-Ru system. [4] Because of that, we expect the model to reproduce the complexity of an equivalent experimental search, and therefore be likely suitable as ap roxy for substituting most of the necessary experiments by simulations.B ys ampling alloy compositions from the model, the number of experiments needed for future composition optimizations can thus be estimated.…”

Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications

“…Simulations have long been used to instigate various phenomena such as the dynamics of biological systems [28], additive and subtractive manufacturing [29] and beyond. In the case of high entropy alloy synthesis, the concept of configurational entropy is well established [30,31]. Based on these theoretical canons, commercial software's are developed to predict the phase, entropy and required temperatures of multicomponent alloys.…”

Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials

“…The advent of machine-learning interatomic potentials (MLIPs) [29,30] has made it possible to significantly reduce the computational costs of pure DFT calculations and to access materials properties with near DFT accuracy while preserving the computational efficiency [31] of classical interatomic potentials [20,32,33]. MLIPs have rapidly advanced in the past decade, resulting in a number of approaches [34][35][36][37][38].…”

“…MLIPs have rapidly advanced in the past decade, resulting in a number of approaches [34][35][36][37][38]. They offer a particularly efficient way to tackle the challenges associated with HEAs [29]. A fundamental requirement of MLIP-based approaches is a well-defined training set which contains information about all relevant and often a priori unknown phases and configurations (see, e.g, Ref.…”