This review discussed the dilemma of small data faced by materials machine learning. First,
we analyzed the limitations brought by small data. Then, the workflow of materials machine …

In the last century, molecular crystals functioned predominantly as a means for determining
the molecular structures via X-ray diffraction, albeit as the century came to a close the …

The assembly of conjugated organic molecules from solution to solid-state plays a critical
role in determining the thin film morphology and optoelectronic properties of solution …

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

The Crystal program for quantum-mechanical simulations of materials has been bridging the
realm of molecular quantum chemistry to the realm of solid state physics for many years …

The large and rapidly growing family of 2D early transition metal carbides, nitrides, and
carbonitrides (MXenes) raises significant interest in the materials science and chemistry of …

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 …

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 …

Entropy-related phase stabilization can allow compositionally complex solid solutions of
multiple principal elements. The massive mixing approach was originally introduced for …

Spin–orbit coupling induces a unique form of Zeeman interaction in momentum space in
materials that lack inversion symmetry: the electron's spin is locked on an effective magnetic …