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

Under the guidance of the material genome initiative (MGI), the use of data‐driven methods
to discover new materials has become an innovation of materials science. The polymer …

Graph neural networks (GNNs) have received intense interest as a rapidly expanding class
of machine learning models remarkably well-suited for materials applications. To date, a …

Mechanical properties consisting of the bulk modulus, shear modulus, Young's modulus,
Poisson's ratio, etc., are key factors in determining the practical applications of MAX phases …

Novel functional materials are urgently needed to help combat the major global challenges
facing humanity, such as climate change and resource scarcity. Yet, the traditional …

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …

The great tunability of the properties of halide perovskites presents new opportunities for
optoelectronic applications as well as significant challenges associated with exploring …

The availability and easy access of large-scale experimental and computational materials
data have enabled the emergence of accelerated development of algorithms and models for …

Superconductivity allows electric conductance with no energy losses when the ambient
temperature drops below a critical value (T c). Currently, the machine learning (ML)-based …

The advent of high-throughput density functional theory (DFT) calculations has supported
the creation of large databases containing the quantitative output necessary for constructing …