Harnessing Data & Machine Learning

Objectives

Nearly every aspect of modern life depends on catalysts, from fuels to synthetic fibers, drugs to detergents, and paints to plastics. Current strategies for developing catalysts rely mostly on time-intensive, trial-and-error experiments. Recent advances in computer science and machine learning have the potential to speed up discovery in this field by automating search mechanisms for these vastly complex and data-rich systems, ultimately revealing hidden patterns and physical properties that scientists can use to design novel catalysts. The goal of the Leonard Lab is to develop novel data mining and extraction methodologies, which will in turn accelerate catalytic insights and innovations with potentially far-reaching advances in challenging chemistries such as water splitting and alkane oxidation.

Principle Project

NRT: Internet of Catalysis

The National Science Foundation's (NSF) Research Traineeship (NRT) grant creates a comprehensive traineeship for STEM graduate students in high priority interdisciplinary convergent research areas. Specifically, this NRT: Internet of Catalysis, seeks to intersect chemical engineering, computer science and chemistry. The students are working together to develop a data base from published research which through applying machine learning algorithms has the potential to generate novel catalyst combinations that could greatly advance the field of catalysis. Explore our NRT program website to learn more information about the program and how to get involved.