Session: Data Science in Catalysis I

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.