The development of machine-learned potentials for catalyst discovery has predominantly
been focused on very specific chemistries and material compositions. While they are …

Catalyst discovery and optimization is key to solving many societal and energy challenges
including solar fuel synthesis, long-term energy storage, and renewable fertilizer production …

Advances in machine learning (ML) are making a large impact in many fields, including:
artificial intelligence, 1 materials science, 2, 3 and chemical engineering. 4 Generally, ML …

The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

Data science and machine learning have the potential to accelerate the discovery of
effective catalysts; however, these approaches are currently held back by the issue of …

The binding site and energy is an invaluable descriptor in high-throughput screening of
catalysts, as it is accessible and correlates with the activity and selectivity. Recently …

High throughput experimentation in heterogeneous catalysis provides an efficient solution to
the generation of large datasets under reproducible conditions. Knowledge extraction from …

Heterogeneous catalysis, for its industrial importance and great complexity in structure, has
long been the testing ground of new characterization techniques. Machine learning (ML) as …

Identifying the structure of heterogeneous catalysts is a critical step to model and understand
catalytic reactions and structure-property relations. Computational predictions of catalyst …

Conspectus Finding catalytic materials with optimal properties for sustainable chemical and
energy transformations is one of the pressing challenges facing our society today …