Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …

Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as
input computed or experimental materials data, ML algorithms predict the structural …

In recent years, the notion of'Quantum Materials' has emerged as a powerful unifying
concept across diverse fields of science and engineering, from condensed-matter and …

Crystal‐graph attention neural networks have emerged recently as remarkable tools for the
prediction of thermodynamic stability. The efficacy of their learning capabilities and their …

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the
materials science toolbox, able to bridge ab initio accuracy with the computational efficiency …

Two-dimensional (2D) materials exhibit a wide range of atomic structures, compositions, and
associated versatility of properties. Furthermore, for a given composition, a variety of …

Although data-driven approaches have made significant strides in various scientific fields,
there has been a lack of systematic summaries and discussions on their application in 2D …

Graph neural networks for crystal structures typically use the atomic positions and the atomic
species as input. Unfortunately, this information is not available when predicting new …

Combining materials science, artificial intelligence (AI), physical chemistry, and other
disciplines, materials informatics is continuously accelerating the vigorous development of …

Magnetic materials have been applied in a large variety of technologies, from data storage
to quantum devices. The development of two-dimensional (2D) materials has opened new …