Academic Journal
A data-driven high-throughput workflow applied to promoted In-oxide catalysts for CO 2 hydrogenation to methanol
| العنوان: | A data-driven high-throughput workflow applied to promoted In-oxide catalysts for CO 2 hydrogenation to methanol |
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| المؤلفون: | 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 |
| المساهمون: | Deutsche Forschungsgemeinschaft |
| المصدر: | Catalysis Science & Technology ; volume 13, issue 9, page 2656-2661 ; ISSN 2044-4753 2044-4761 |
| بيانات النشر: | Royal Society of Chemistry (RSC) |
| سنة النشر: | 2023 |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1039/d3cy00148b |
| الاتاحة: | http://dx.doi.org/10.1039/d3cy00148b http://pubs.rsc.org/en/content/articlepdf/2023/CY/D3CY00148B |
| Rights: | http://creativecommons.org/licenses/by/3.0/ |
| رقم الانضمام: | edsbas.1FC1E084 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1039/d3cy00148b |
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