Developing Catalysts via Structure‐Property Relations Discovered by Machine Learning: An Industrial Perspective. Issue 11 (11th October 2022)

• Record Type: Journal Article
• Authors: Joshi, Hrishikesh; Wilde, Nicole; Asche, Thomas S.; Wolf, Dorit
• Other Names: Beller Matthias guestEditor.; Bender Michael guestEditor.; Bornscheuer Uwe T. guestEditor.; Schunk Stephan guestEditor.
• Is Part Of: Chemie Ingenieur Technik. Volume 94:Issue 11(2022)
• Page Start: 1645
• Page End: 1654
• Publication Date: 2022-10-11
• Subjects: Heterogeneous -- Industrial catalyst -- Machine learning -- Prediction -- Relationships -- Structure‐property; Chemical engineering -- Patents -- Periodicals; Chemical engineering -- Periodicals; Chemical industry -- Periodicals; Chemistry, Technical -- Periodicals; 660.05
• DOI: 10.1002/cite.202200071
• Languages: English
• ISSNs: 0009-286X

Abstract

Abstract: 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. Abstract : 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.