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 …

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 …

Electroluminescence efficiencies of metal halide perovskite nanocrystals (PNCs) are limited
by a lack of material strategies that can both suppress the formation of defects and enhance …

Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a
fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow …

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

Graph networks are a new machine learning (ML) paradigm that supports both relational
reasoning and combinatorial generalization. Here, we develop universal MatErials Graph …

Solid-state batteries have fascinated the research community over the past decade, largely
due to their improved safety properties and potential for high-energy density. Searching for …

Abinit is a material-and nanostructure-oriented package that implements density-functional
theory (DFT) and many-body perturbation theory (MBPT) to find, from first principles …

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 …

Mechanical metamaterials are man-made structures with counterintuitive mechanical
properties that originate in the geometry of their unit cell instead of the properties of each …