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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

Ying Zhao, Shuaishuai Gao, Penghui Ren, Lishuang Ma, Xuebo Chen

2025Journal of Materials Chemistry A21 citationsDOI

Abstract

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.

Topics & Concepts

HeteroatomThroughputCatalysisKey (lock)DopingHydrogenAtom (system on chip)Materials scienceCombinatorial chemistryNanotechnologyComputer scienceChemistryOptoelectronicsOrganic chemistryTelecommunicationsWirelessEmbedded systemRing (chemistry)Computer securityElectrocatalysts for Energy ConversionMachine Learning in Materials ScienceAdvanced Photocatalysis Techniques
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 | Litcius