Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

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

The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

Electrocatalysis for the oxygen evolution reactions (OER) has attracted much attention due
to its important role in water splitting and rechargeable metal-air batteries. Therefore …

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, 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 …

Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

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

Spectroscopic techniques can probe molecular systems non-invasively and investigate their
structure, properties and dynamics in different environments and physico-chemical …

On the way toward a sustainable energy economy, electrode materials that do not contain
scarce noble metals need to be developed. Operating at low computational costs, material …