Division/Committee: [COMP] Division of Computers in Chemistry

Heterogeneous and homogeneous catalysis are instrumental in developing commercial materials, which serve a wide range of applications including packaging, health and hygiene, consumer goods. These industries are currently being challenged with developing materials that are cheap, more sustainable (lower carbon footprint) and that can support a circular economy. To meet these challenges, it is increasingly important to explore a wide range of catalytic routes and to invent more robust (less prone to deactivation) catalysts. Traditionally, synthetic chemists and chemical engineers collaborated with computational materials scientists to develop and optimize the activity, selectivity and degradation of catalysts using quantum chemistry, statistical mechanics and process optimization. In the past few years, data science related methods (machine learning and deep learning) have had an increasing role in developing and optimizing new catalysts. This symposium will cover application of data science techniques in (i) expediting catalyst synthesis, (ii) discovering new catalysts and (iii) process optimization.

Jim Pfaendtner

University of Washington

Appearances