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 …

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 …

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 …

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 …

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

By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

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

Electrochemical coupling between carbon and nitrogen species to generate high-value C–N
products, including urea, presents significant economic and environmental potentials for …

Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …

The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …