High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery. Issue 20 (12th May 2020)

• Record Type: Journal Article
• Authors: McCullough, Katherine; Williams, Travis; Mingle, Kathleen; Jamshidi, Pooyan; Lauterbach, Jochen
• Is Part Of: Physical chemistry chemical physics. Volume 22:Issue 20(2020)
• Page Start: 11174
• Page End: 11196
• Publication Date: 2020-05-12
• Subjects: Chemistry, Physical and theoretical -- Periodicals; 541.3
• DOI: 10.1039/d0cp00972e
• Languages: English
• ISSNs: 1463-9076

Abstract

Abstract : High throughput experimentation in heterogeneous catalysis provides an efficient solution to the generation of large datasets under reproducible conditions. Abstract : High throughput experimentation in heterogeneous catalysis provides an efficient solution to the generation of large datasets under reproducible conditions. Knowledge extraction from these datasets has mostly been performed using statistical methods, targeting the optimization of catalyst formulations. The combination of advanced machine learning methodologies with high-throughput experimentation has enormous potential to accelerate the predictive discovery of novel catalyst formulations that do not exist with current statistical design of experiments. This perspective describes selective examples ranging from statistical design of experiments for catalyst synthesis to genetic algorithms applied to catalyst optimization, and finally random forest machine learning using experimental data for the discovery of novel catalysts. Lastly, this perspective also provides an outlook on advanced machine learning methodologies as applied to experimental data for materials discovery.