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 exploration of catalysts for energy conversion lies at the center of sustainable
development. The combination of experimental and computational approaches can provide …

The WIEN2k program is based on the augmented plane wave plus local orbitals (APW+ lo)
method to solve the Kohn–Sham equations of density functional theory. The APW+ lo …

Abstract Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While
CSP can be addressed by employing currently-prevailing generative models (eg diffusion …

Generating the periodic structure of stable materials is a long-standing challenge for the
material design community. This task is difficult because stable materials only exist in a low …

Metal halide perovskites are the first solution processed semiconductors that can compete in
their functionality with conventional semiconductors, such as silicon. Over the past several …

We introduce the Computational 2D Materials Database (C2DB), which organises a variety
of structural, thermodynamic, elastic, electronic, magnetic, and optical properties of around …

The use of machine learning methods for accelerating the design of crystalline materials
usually requires manually constructed feature vectors or complex transformation of atom …

The generation of plausible crystal structures is often the first step in predicting the structure
and properties of a material from its chemical composition. However, most current methods …

The conversion of sunlight into fuels and chemicals is an attractive prospect for the storage
of renewable energy, and photoelectrocatalytic technologies represent a pathway by which …