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 …

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

There is an urgent global need for electrochemical energy storage that includes materials
that can provide simultaneous high power and high energy density. One strategy to achieve …

The Sabatier principle, which states that the binding energy between the catalyst and the
reactant should be neither too strong nor too weak, has been widely used as the key …

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

Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent
years. In this Review, we studied the growth and distribution of AI-related chemistry …

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

Atomistic simulation of the electrochemical double layer is an ambitious undertaking,
requiring quantum mechanical description of electrons, phase space sampling of liquid …