Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models
Daniel Schwalbe‐Koda, Nitish Govindarajan, Joel B. Varley
Abstract
A combination of generalization in neural networks and fast data pipelines enables comprehensive sampling coverage and co-adsorption effects in heterogeneous catalyst models.
Topics & Concepts
GeneralizationArtificial neural networkSampling (signal processing)Computer scienceArtificial intelligenceMachine learningData miningMathematicsTelecommunicationsMathematical analysisDetectorMachine Learning in Materials ScienceCatalytic Processes in Materials ScienceCatalysis and Oxidation Reactions