Local descriptors-based machine learning model refined by cluster analysis for accurately predicting adsorption energies on bimetallic alloys
Andrés Felipe Usuga, C. S. Praveen, Aleix Comas‐Vives
Abstract
The CatBoost method, combined with cluster filtering, accurately predicts adsorption energies on metal alloys. The approach uses local chemical descriptors to understand chemisorption on metal alloys, which is essential for catalytic applications.
Topics & Concepts
Bimetallic stripChemisorptionCluster (spacecraft)AdsorptionCluster analysisMetalArtificial intelligenceMaterials scienceComputer scienceMachine learningChemistryMetallurgyPhysical chemistryProgramming languageMachine Learning in Materials ScienceElectrocatalysts for Energy ConversionElectrochemical Analysis and Applications