Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Improvements in the efficiency and availability of quantum chemistry codes, supercomputing
centers, and open materials databases have transformed the accessibility of computational …

Despite the rise in computing power, the large space of possible combinations of elements
and crystal structure types makes large-scale high-throughput surveys of stable materials …

Recommender systems (RS) are effective tools for mitigating information overload and have
seen extensive applications across various domains. However, the single focus on utility …

Emerging machine-learned models have enabled efficient and accurate prediction of
compound formation energy, with the most prevalent models relying on graph structures for …

In the search for novel intermetallic ternary alloys, much of the effort goes into performing a
large number of ab initio calculations covering a wide range of compositions and structures …

The discovery of new materials in unexplored chemical spaces necessitates quick and
accurate prediction of thermodynamic stability, often assessed using density functional …

Novel wide-band-gap semiconductors are needed for next-generation power electronics,
but there is a gap between a promising material and a functional device. Finding stable …

In material informatics, the representation of the material structure is fundamentally essential
to obtaining better prediction results, and graph representation has attracted much attention …

First-principles computations reliably predict the energetics of point defects in
semiconductors but are constrained by the expense of using large supercells and advanced …