Machine Learning for High-Entropy Catalysts: Methods and Applications
Abstract
High-entropy alloys (HEAs) exhibit exceptional catalytic performance in various reactions due to their high configurational entropy, synergistic elemental effects, tunable electronic structures, and excellent structural stability. However, the vast compositional space of HEA catalysts makes traditional experimental and theoretical design costly and inefficient. In recent years, data-driven machine learning (ML) methods have emerged as powerful tools for studying HEAs in catalysis. Through predictive models and ML surrogates, researchers can decipher the intricate composition-structure-performance relationships of these materials. In addition, by leveraging large language models (LLMs) for knowledge extraction, hypothesis generation and validation, and as a foundation to build integrated design workflows, ML approaches can significantly accelerate the design of novel HEA catalysts. This review systematically summarizes the latest methodological advances and applications of ML methods for HEAs in catalysis, discusses the challenges, and offers insights into future research directions to support the rational design and efficient development of catalysts.
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BibTeX
@misc{chen2026machine,
title = {{Machine Learning for High-Entropy Catalysts: Methods and Applications}},
author = {Hao Chen and Zongrui Pei and Xianglin Liu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.19837},
eprint = {2609.19837},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.19837},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Machine Learning for High-Entropy Catalysts: Methods and Applications
AU - Hao Chen
AU - Zongrui Pei
AU - Xianglin Liu
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2609.19837
UR - https://arxiv.org/abs/2609.19837
AB - High-entropy alloys (HEAs) exhibit exceptional catalytic performance in various reactions due to their high configurational entropy, synergistic elemental effects, tunable electronic structures, and excellent structural stability. However, the vast compositional space of HEA catalysts makes traditional experimental and theoretical design costly and inefficient. In recent years, data-driven machine learning (ML) methods have emerged as powerful tools for studying HEAs in catalysis. Through predictive models and ML surrogates, researchers can decipher the intricate composition-structure-performance relationships of these materials. In addition, by leveraging large language models (LLMs) for knowledge extraction, hypothesis generation and validation, and as a foundation to build integrated design workflows, ML approaches can significantly accelerate the design of novel HEA catalysts. This review systematically summarizes the latest methodological advances and applications of ML methods for HEAs in catalysis, discusses the challenges, and offers insights into future research directions to support the rational design and efficient development of catalysts.
ER -