Publication: A data-driven high-throughput workflow applied to promoted In-oxide catalysts for CO2 hydrogenation to methanol

Authors: Mohammad Khatamirad, Edvin Fako, Chiara Boscagli, Matthias Müller, Fabian Ebert, Raoul Naumann d'Alnoncourt, Ansgar Schaefer, Stephan Andreas Schunk (hte GmbH), Ivana Jevtovikj, Frank Rosowkski, Sandip De

Catal. Sci. Technol., 2023
13, 2656, 27 March 2023

We propose a novel high-throughput workflow, combining DFT-derived atomic scale interaction parameters with experimental data to identify key performance-related descriptors in a CO₂ to methanol reaction, for In-based catalysts. Utilizing advanced machine learning algorithms suitable for small datasets, secondary descriptors with high predictive power for catalytic activity were constructed. These descriptors, which highlight the crucial role of hydroxyl sites, can be applied to designing new materials and to bringing them to the test with high-throughput screening, paving the path for accelerated catalyst design.