Imbalanced data sets in materials informatics are pervasive and pose a challenge to the
development of classification models. This work investigates crystal point group prediction …

Abstract Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which
has affected the economic life globally. On the one hand, numerous elements in the …

Machine learning is a powerful tool to predict the properties of materials for a variety of
applications. However, generating data sets of carefully characterized materials can be time …

Abstract Machine learning is a valuable tool that can accelerate the discovery and design of
materials occupying combinatorial chemical spaces. However, the prerequisite need for vast …

In materials informatics and computational design, ionic radius is an essential physical
feature needed to predict and model structures and other material properties. Currently, the …

Predicting crystal structure from the chemical composition is one of the most challenging and
long-standing problems in condensed matter physics. This problem resides at the interface …

Ionic radii play a key descriptor role in the field of material informatics and crystallography.
Traditionally, improving the widely used Shannon's radii dataset has primarily involved …

Materials informatics uses machine learning to predict the properties of new materials, but
generally requires extensive characterisation and feature extraction to describe the input …

Predicting crystal symmetry simply from chemical composition has remained challenging.
Several machine-learning approaches can be employed, but the predictive value of popular …

In the density functional theory (DFT), it is well-known that the generalized gradient
approximation (GGA) underestimates the energy gap. One of the commonly used GGA …