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 …

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

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 …

Global ammonia production reached 175 million metric tons in 2016, 90% of which is
produced from high purity N2 and H2 gases at high temperatures and pressures via the …

Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …

Single-atom alloys (SAAs) play an increasingly significant role in the field of single-site
catalysis and are typically composed of catalytically active elements atomically dispersed in …

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 …

The Sabatier principle is widely explored in heterogeneous catalysis, graphically depicted in
volcano plots. The most desirable activity is located at the peak of the volcano, and further …

Photocatalytic production of CH4 from CO2 and H2O is recognized as one of the most
sustainable approaches to address the environmental and energy crises. However, this …

Selective catalytic hydrogenation has wide applications in both petrochemical and fine
chemical industries, however, it remains challenging when two or multiple functional groups …