Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and
energy conversion processes, and its revolution necessitates the hunt for new materials with …

Machine learning (ML) promises to increase the efficiency of screening a large number of
materials for catalytic reactions. However, most existing ML models can only be applied to a …

Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, ie, a linear scaling between reaction
and activation energies, lie at the core of computational design of heterogeneous catalysts …

Machine-learning models for predicting adsorption energies on metallic surfaces often rely
on basic elemental properties and electronic and geometric descriptors. Here, we apply …

A catalytic surface should be stable under reaction conditions to be effective. However, it
takes significant effort to screen many surfaces for their stability, as this requires intensive …

Surface Pourbaix diagrams are critical to understanding the stability of nanomaterials in
electrochemical environments. Their construction based on density functional theory is …

Catalyst screening is a critical step in the discovery and development of heterogeneous
catalysts, which are vital for a wide range of chemical processes. In recent years …

We introduce a combined fast semi-empirical quantum mechanical and machine learning
(SQM/ML) approach capable of matching the C–H bond dissociation enthalpies (BDEs) …

Surface adsorption is one of the fundamental processes in numerous fields, including
catalysis, the environment, energy and medicine. The development of an adsorption model …

Artificial intelligence (AI) is playing an increasingly large role in catalysis, similar to other
aspects of manufacturing. In particular, modern data science and machine learning (ML) …