A data-driven high-throughput workflow applied to promoted In-oxide catalysts for CO<sub>2</sub> hydrogenation to methanol
Mohammad Khatamirad, Edvin Fako, Chiara Boscagli, Matthias Müller, Fabian Ebert, Raoul Naumann d’Alnoncourt, Ansgar Schaefer, Stephan A. Schunk, Ivana Jevtovikj, Frank Rosowski, Sandip De
Abstract
To facilitate accelerated catalyst design, a combined computation and experimental workflow based on machine learning algorithms is proposed, which detects key performance-related descriptors in a CO 2 to methanol reaction, for In 2 O 3 -based catalysts.
Topics & Concepts
WorkflowCatalysisMethanolThroughputComputationKey (lock)Computer scienceOxideChemistryDatabaseOrganic chemistryAlgorithmOperating systemWirelessCatalytic Processes in Materials ScienceCatalysis and Oxidation ReactionsMachine Learning in Materials Science