Active learning-driven quantitative synthesis–structure–property relations for improving performance and revealing active sites of nitrogen-doped carbon for the hydrogen evolution reaction
Elvis Osamudiamhen Ebikade, Yifan Wang, Nicholas Samulewicz, Bjorn Hasa, Dionisios G. Vlachos
Abstract
A data-driven quantitative synthesis–structure–property relation methodology to elucidate correlations between catalyst synthesis conditions, structural properties and observed performance, providing a systematic way to optimize practical catalysts.
Topics & Concepts
CatalysisProperty (philosophy)HydrogenNitrogenCarbon fibersMaterials scienceRelation (database)Active siteNanotechnologyComputer scienceChemical engineeringBiological systemChemistryEngineeringOrganic chemistryData miningComposite materialComposite numberEpistemologyBiologyPhilosophyMachine Learning in Materials ScienceElectrocatalysts for Energy ConversionComputational Drug Discovery Methods