高熵催化剂机器学习：方法与应用

Condensed Matter > Materials Science arXiv:2609.19837 (cond-mat) [Submitted on 17 Sep 2026] Title:Machine Learning for High-Entropy Catalysts: Methods and Applications Authors:Hao Chen, Zongrui Pei, Xianglin Liu View a PDF of the paper titled Machine Learning for High-Entropy Catalysts: Methods and Applications, by Hao Chen and Zongrui Pei and Xianglin Liu View PDF HTML (experimental) 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. Subjects: Materials Science (cond-mat.mtrl-sci) Cite as: arXiv:2609.19837 [cond-mat.mtrl-sci] (or arXiv:2609.19837v1 [cond-mat.mtrl-sci] for this version) https://doi.org/10.48550/arXiv.2609.19837 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xianglin Liu [view email] [v1] Thu, 17 Sep 2026 07:42:33 UTC (2,709 KB) Full-text links: Access Paper: View a PDF of the paper titled Machine Learning for High-Entropy Catalysts: Methods and Applications, by Hao Chen and Zongrui Pei and Xianglin Liu View PDF HTML (experimental) TeX Source view license Current browse context: cond-mat.mtrl-sci new | recent | 2026-09 Change to browse by: cond-mat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)