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Interpretable Machine Learning Framework Deciphers the Role of Local Environment of High-Entropy Intermetallic Compounds for Alkaline Hydrogen Evolution Reaction
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DOI：10.1002/aenm.71361.png)
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Abstract
En 中文
The application of high-entropy intermetallic (HEI) compounds in the field of catalysis has attracted widespread attention, but their huge material space seriously hinders experimental exploration. Herein, we for the first time reported the efficient design, screening, and prediction of a great deal of high-performance HER catalysts from a huge HEI material space (106) based on our newly established machine learning (ML) driven “decode—describe—design” (3D) framework by experimentally fabricated A3B-type (FeCoNi)3(AlTi) system. Over 700 catalysts exhibited better performance than existing experimental results, indicating that the experiment only touched a very small part of the material space. Moreover, we developed various powerful descriptors (such as ΛOH, ΛH) and analysis tools (such as, RDERA, RPDAD, CPMCC, CPMCS) to decouple the complex interplay of elements into atomic- and region-specific effects, laying the foundation for the establishment of structure-activity relationships and guiding the rational design of catalysts. The interpretable ML-driven 3D framework, powerful descriptors, and novel analysis tools enable efficient design and screening, catalytic mechanism elucidation, and structure-activity relationship establishment. They are expected to stimulate further computational and experimental investigations in related catalyst systems.
Keywords:
alkaline hydrogen evolution reaction
high-entropy intermetallic compounds
interpretable machine learning framework
physical descriptors and analysis tools
rational design and screening of catalysts
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