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-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.

Automated summary

Industrial catalyst development is a complex issue that can be optimized with machine learning strategies, which offer a more efficient alternative to conventional methods.