Catalysis
informatics is a distinct subfield that lies at the intersection
of cheminformatics and materials informatics but with distinctive
challenges arising from the dynamic, surface-sensitive, and multiscale
nature of heterogeneous catalysis. The ideas behind catalysis informatics
can be traced back decades, but the field is only recently emerging
due to advances in data infrastructure, statistics, machine learning,
and computational methods. In this work, we review the field from
early works on expert systems and knowledge engines to more recent
approaches utilizing machine-learning and uncertainty quantification.
The data–information–knowledge hierarchy is introduced
and used to classify various developments. The chemical master equation
and microkinetic models are proposed as a quantitative representation
of catalysis knowledge, which can be used to generate explanative
and predictive hypotheses for the understanding and discovery of catalytic
materials. We discuss future prospects for the field, including improved
quantitative coupling of experiment/theory, advanced microkinetic
models, and the development of open-source software tools. Ultimately,
integration of existing chemical and physical models with emerging
statistical and computational tools presents a promising route toward
the automated design, discovery, and optimization of heterogeneous
catalytic processes.