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

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

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 …

Concrete, as the most widely used construction material, is inextricably connected with
human development. Despite conceptual and methodological progress in concrete science …

Artificial intelligence (AI)-based methods continue to make inroads into accelerated
materials design and development. Here, we review AI-enabled advances made in the …

Skin-interfaced electronics is gradually changing medical practices by enabling continuous
and non-invasive tracking of physiological and biochemical information. With the rise of big …

Large-scale atomistic computer simulations of materials rely on interatomic potentials
providing computationally efficient predictions of energy and Newtonian forces. Traditional …

For the proper design and evaluation of next‐generation lithium‐ion batteries, different
physical‐chemical scales have to be considered. Taking into account the electrochemical …

Graph neural networks (GNNs) have received intense interest as a rapidly expanding class
of machine learning models remarkably well-suited for materials applications. To date, a …

Electronic skins (e-skins) have seen intense research and rapid development in the past two
decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors …