Finding the Pareto front for high-entropy-alloy catalysts

Chemical Science 2026 Open access

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Authors: Chengyi Zhang, Ruihu Lu, Qi Sun, Yu Mao, Tilo Söhnel, Yan Zhao, Donald G. Truhlar, Ziyun Wang
DOI: 10.1039/d5sc06100h
Cited by: 6
References: 60

Abstract

. Here, we provide a method to optimize the front by designing a multi-objective genetic algorithm that combines machine learning, graph neural network calculations, and density functional calculations. The application considered is the oxygen evolution reaction catalyzed by high-entropy alloys. We find that the Pareto front generally contains alloys with diverse elements, but that enhancing stability inevitably inflicts a toll on activity. We compare the general conclusions of our work to a survey of 545 experiments.

Fields of Study & Subjects

High Entropy Alloys Studies Catalysis and Oxidation Reactions Machine Learning in Materials Science

Publication details

Journal / source
Chemical Science
Publisher
Royal Society of Chemistry (RSC)
Volume
17
Issue
9
Pages
4744–4752
Publication date
1 January 2026
Language
EN