Heterogeneous catalysis underpins a wide variety of industrial processes including energy
conversion, chemical manufacturing and environmental remediation. Significant advances …

Most applications of machine learning in heterogeneous catalysis thus far have used black-
box models to predict computable physical properties (descriptors), such as adsorption or …

Metastable metal oxides with ribbon morphologies have promising applications for energy
conversion catalysis, however they are largely restricted by their limited synthesis methods …

Exploring new materials is essential in the field of material science. Especially, searching for
optimal materials with utmost atomic utilization, ideal activities and desirable stability for …

Proton-coupled electron transfer (PCET) plays an essential role in a wide range of
electrocatalytic processes. A vast array of theoretical and computational methods have been …

The development of machine learning models for electrocatalysts requires a broad set of
training data to enable their use across a wide variety of materials. One class of materials …

Layered double hydroxides (LDHs) are among the most active and studied catalysts for the
oxygen evolution reaction (OER) in alkaline electrolytes. However, previous studies have …

The design of heterogeneous catalysts is necessarily surface‐focused, generally achieved
via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure …

Proton exchange membrane water electrolyzers (PEMWEs) driven by renewable electricity
provide a facile path toward green hydrogen production, which is critical for establishing a …

The electrocatalytic oxygen evolution reaction (OER) supplies the protons and electrons
needed to transform renewable electricity into chemicals and fuels. However, the OER is …