Applications of Data Science in Catalysis and Reaction Engineering II

There has been a tremendous increase in the application of data science algorithms and tools to problems in catalysis and reaction engineering in the last few years. This session invites submissions pertaining to the application of deep learning learning, data mining, informatics, and other data-driven methods in the context of analysis, modeling, and design of any reaction system (including but not limited to heterogeneous catalysis and combustion). Topics of interest include: design of heterogeneous and homogeneous catalysts using experimental and ab initio data, development of surrogate functions for rapid exploration of the reaction potential energy surface, development of quantitative structure-reactivity relationships from ab initio, kinetic, and spectroscopic data, design of experiments and computations (active learning) in the context of mechanism elucidation and catalyst design, uncertainty quantification, data-driven mechanistic and reactor-level modeling of complex reaction systems, and open source software, database, and automated workflows for generation, analysis, and storage of vast quantities of data pertinent to reaction systems.