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 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 …

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

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 …

The design of functional materials with desired properties is essential in driving
technological advances in areas like energy storage, catalysis, and carbon capture …

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 …

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

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

Metallenes, atomically thin layers composed primarily of under-coordinated metal atoms,
have emerged as the newest members in the family of 2D nanomaterials. Furthermore, the …