Abstract Machine Learning (ML) and Deep Learning (DL) have achieved high success in
many textual, auditory, medical imaging, and visual recognition patterns. Concerning the …

High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …

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 …

Metasurfaces, interacted with artificial intelligence, have now been motivating many
contemporary research studies to revisit established fields, eg, direction of arrival (DOA) …

DNA can stabilize silver nanoclusters (Ag N-DNAs) whose atomic sizes and diverse
fluorescence colors are selected by nucleobase sequence. These programmable …

As an implementation tool of data intensive scientific research methods, machine learning
(ML) can effectively shorten the research and development (R&D) cycle of new materials by …

Conspectus Mechanical hardness is a physical property used to gauge the applications of
materials in the manufacturing and machining industries. Because of their high hardness …

Discovering new materials with desired properties has been a dominant and crucial topic of
interest in the field of materials science in the past few decades. In this work, novel carbon …

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 …