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 …

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …

Bimetallic platinum-nickel (Pt-Ni) nanostructures represent an emerging class of
electrocatalysts for oxygen reduction reaction (ORR) in fuel cells, but practical applications …

The field of laser-matter interaction traditionally deals with the response of atoms, molecules,
and plasmas to an external light wave. However, the recent sustained technological …

The use of supervised machine learning to develop fast and accurate interatomic potential
models is transforming molecular and materials research by greatly accelerating atomic …

The bulk-boundary correspondence is crucial to topological insulators. It associates the
existence of boundary states (with zero energy and possessing chiral or helical properties) …

Typically, computational screens for new materials sharply constrain the compositional
search space, structural search space, or both, for the sake of tractability. To lift these …

Since its development in the 1970s, the rechargeable alkali-ion battery has proven to be a
truly transformative technology, providing portable energy storage for devices ranging from …

This chapter addresses the role that data‐driven approaches, especially machine learning
methods, are expected to play in materials research in the immediate future. Machine …

Artificial intelligence (AI) is gaining strength, and materials science can both contribute to
and profit from it. In a simultaneous progress race, new materials, systems, and processes …