High-throughput screening and an interpretable machine learning model of single-atom hydrogen evolution catalysts with an asymmetric coordination environment constructed from heteroatom-doped graphdiyne

作者：Ying Zhao, Shuaishuai Gao, Penghui Ren, Lishuang Ma, Xuebo Chen · 发表于：Journal of Materials Chemistry A · 年份：2025 · DOI：10.1039/d4ta08095e · 被引用次数：23 · 研究领域：Electrocatalysts for Energy Conversion、Machine Learning in Materials Science、Advanced Photocatalysis Techniques

Exploring high-activity and low-cost electrocatalysts for the hydrogen evolution reaction is the key to developing new energy sources, but it faces major challenges.