Issue 9, 2023

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

Abstract

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.

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

Supplementary files

Article information

Article type
Communication
Submitted
31 jan 2023
Accepted
27 mrt 2023
First published
27 mrt 2023
This article is Open Access
Creative Commons BY license

Catal. Sci. Technol., 2023,13, 2656-2661

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

M. Khatamirad, E. Fako, C. Boscagli, M. Müller, F. Ebert, R. Naumann d'Alnoncourt, A. Schaefer, S. A. Schunk, I. Jevtovikj, F. Rosowski and S. De, Catal. Sci. Technol., 2023, 13, 2656 DOI: 10.1039/D3CY00148B

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