Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

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 …

Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Adsorption energy (AE) of reactive intermediate is currently the most important descriptor for
electrochemical reactions (eg, water electrolysis, hydrogen fuel cell, electrochemical …

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 …

The solar‐energy‐driven photoreduction of CO2 has recently emerged as a promising
approach to directly transform CO2 into valuable energy sources under mild conditions. As a …

Since the seminal works on the application of density functional theory and the
computational hydrogen electrode to electrochemical CO2 reduction (eCO2R) and …

The unprecedented ability of computations to probe atomic-level details of catalytic systems
holds immense promise for the fundamentals-based bottom-up design of novel …

High‐entropy alloys (HEAs) are expected to function well as electrocatalytic materials, owing
to their widely adjustable composition and unique physical and chemical properties …