Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

We review the GPAW open-source Python package for electronic structure calculations.
GPAW is based on the projector-augmented wave method and can solve the self-consistent …

Recent years have seen vast progress in the development of machine learned force fields
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …

Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …

Progress in the discovery of new materials has been accelerated by the development of
reliable quantum-mechanical approaches to crystal structure prediction. The properties of a …

Two hydrogen-rich materials, H 3 S and LaH 10, synthesized at megabar pressures, have
revolutionized the field of condensed matter physics providing the first glimpse to the …

Predicting the stability of the perovskite structure remains a long-standing challenge for the
discovery of new functional materials for many applications including photovoltaics and …

Electrocatalytic CO2 reduction to higher-value hydrocarbons beyond C1 products is
desirable for applications in energy storage, transportation and the chemical industry. Cu …

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 …