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 …

Data‐driven science is heralded as a new paradigm in materials science. In this field, data is
the new resource, and knowledge is extracted from materials datasets that are too big or …

The accuracy and efficiency of electronic-structure methods to understand, predict and
design the properties of materials has driven a new paradigm in research. Simulations can …

In metals additive manufacturing (AM), materials and components are concurrently made in
a single process as layers of metal are fabricated on top of each other in the near-final …

The atomic simulation environment (ASE) is a software package written in the Python
programming language with the aim of setting up, steering, and analyzing atomistic …

Abstract Two-dimensional (2D) materials have emerged as promising candidates for next-
generation electronic and optoelectronic applications. Yet, only a few dozen 2D materials …

Rational design of compounds with specific properties requires understanding and fast
evaluation of molecular properties throughout chemical compound space—the huge set of …

This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …

Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …

To improve the performance of metallic catalysts, alloying provides an efficient methodology
to design state‐of‐the‐art materials. As emerging functional materials, rare‐earth metal …