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

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 CO 2 to methanol reaction, for In-based catalysts. Utilizing advanced machine learning algorithms suitable for small data...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Catalysis science & technology 2023-05, Vol.13 (9), p.2656-2661
Hauptverfasser:	Khatamirad, Mohammad, Fako, Edvin, Boscagli, Chiara, Müller, Matthias, Ebert, Fabian, Naumann d'Alnoncourt, Raoul, Schaefer, Ansgar, Schunk, Stephan Andreas, Jevtovikj, Ivana, Rosowski, Frank, De, Sandip
Format:	Artikel
Sprache:
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!