Abstract Machine Learning (ML) appears to have become the main and foremost approach
to both tackle the hurdles and exploit the opportunities of The Information Age. We present …

Hall-Petch slope (k) is an important material parameter, while there is a great challenge to
accurately predict the k value of magnesium alloys due to a high dependence of k on the …

Predicting material properties by solving the Kohn-Sham (KS) equation, which is the basis of
modern computational approaches to electronic structures, has provided significant …

Exploring the data-driven prediction strategy of physical and chemical properties is attractive
for the rational design of crystal dielectrics with target characteristics, especially for the …

Abstract The use of Electrochemical Impedance Spectroscopy on rechargeable Lithium-ion
battery characterization is an extensively recognized non-destructive procedure for both in …

The lowest-energy structures of AgCu nanoalloys are searched for by global optimization
algorithms for sizes 100 and 200 atoms depending on composition. Even though the AgCu …

In this study, we built a variety of Machine Learning (ML) systems over 23 different sizes of
CH 3 NH 3 PbI 3 perovskite nanoparticles (NPs) to predict the atoms in the NPs from their …

In this study, Nb 2 O 5: ZnO nanocomposite thin film has been deposited by thermionic
vacuum arc and then surface and optical properties of Nb 2 O 5: ZnO nanocomposite thin …

This interdisciplinary study is conducted to find answers to two important questions which
researchers often face in Machine Learning (ML) and Material Science (MS) fields. In this …

With the fission barrier height information, the survival probabilities of super-heavy nuclei
can also be reached. Therefore, it is important to have accurate knowledge of fission …