We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Chemical reaction networks form the heart of microkinetic models, which are one of the key
tools available for gaining detailed mechanistic insight into heterogeneous catalytic …

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

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry
for the creation and optimization of electrocatalysts, which enhance key electrochemical …

Abstract Machine learning (ML) is emerging as a powerful tool for identifying quantitative
structure–activity relationships to accelerate electrocatalyst design by learning from historic …

Graph neural networks (GNNs) have emerged as a powerful machine learning approach for
the prediction of molecular properties. In particular, recently proposed advanced GNN …

Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for
studying molecular mechanisms in the condensed phase, however, they are too expensive …

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and
interfaces bestow them with various exceptional properties. These properties, however, also …

The anodic four-electron oxygen evolution reaction (OER) corresponds to the limiting
process in acidic or alkaline electrolyzers to produce gaseous hydrogen at the cathode of …

Abstract Machine learning (ML) algorithms are currently emerging as powerful tools in all
areas of science. Conventionally, ML is understood as a fundamentally data‐driven …