Developing Catalysts via Structure‐Property Relations Discovered by Machine Learning: An Industrial Perspective

Industrial catalyst development is a complex issue that requires optimization of performance, synthesis, costs, and engineering aspects. During the development, structure‐property relations are often used to provide valuable insights into the catalyst. However, conventionally, this process is time‐c...

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Bibliographic Details
Published inChemie ingenieur technik Vol. 94; no. 11; pp. 1645 - 1654
Main Authors Joshi, Hrishikesh, Wilde, Nicole, Asche, Thomas S., Wolf, Dorit
Format Journal Article
LanguageEnglish
Published 01.11.2022
Subjects
Heterogeneous
Industrial catalyst
Machine learning
Prediction
Relationships
Structure‐property
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Summary:Industrial catalyst development is a complex issue that requires optimization of performance, synthesis, costs, and engineering aspects. During the development, structure‐property relations are often used to provide valuable insights into the catalyst. However, conventionally, this process is time‐consuming and costly. Advancements in the field of automation for experimentation, data collection, and simulations have allowed the use of machine learning (ML) strategies for this development. Herein we provide an industrial perspective on ML strategies for the development of solid catalysts. Industrial catalyst development is a complex issue that requires optimization of performance, synthesis, costs, and engineering aspects. Conventionally, this process is time‐consuming and costly, which provides a huge scope for the implementation of machine learning (ML) strategies. Herein we provide an industrial perspective on these ML strategies.
ISSN:0009-286X
1522-2640
DOI:10.1002/cite.202200071