Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys
Jingzi Zhang, Yuelin Wang, Xuyan Zhou, Chengquan Zhong, Ke Zhang, Jiakai Liu, Kailong Hu, Xi Lin
Abstract
| values less than 0.1 eV are successfully screened out of 2290 candidates selected from the Material Project (MP) database. It is reasonable to expect that the ML models with structural and electronic feature engineering developed in this work would provide new insights in future electrocatalyst developments for the HER and other heterogeneous reactions.
Topics & Concepts
CatalysisAlloyFeature (linguistics)Materials scienceSpace (punctuation)Electronic structureComputer scienceBiological systemNanotechnologyArtificial intelligenceComputational chemistryMetallurgyChemistryOrganic chemistryLinguisticsBiologyOperating systemPhilosophyMachine Learning in Materials ScienceElectrocatalysts for Energy ConversionCatalysis and Hydrodesulfurization Studies