Data science and machine-learning techniques are becoming increasingly relevant to many aspects of the field of catalysis including rapidly predicting atomic-scale information, reducing complexity in large reaction networks, and in the analysis of experimental data. This session will focus broadly on the use of statistical and data-driven approaches that provide insight into catalytic processes. This includes approaches that utilize statistical and machine-learning models to accelerate computational techniques, quantify uncertainty in computational or experimental approaches, produce additional insight from experimental data, or quantitatively couple experimental and computational data.

Catalysis and Reaction Engineering Division

Computational Molecular Science and Engineering Forum (21)