Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

(a) The workflow contains three parts: ML model selection to calculate the expected improvement (EI) values of the target properties for a given alloy; the non-dominated sorting genetic algorithm ...

Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development.

Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed ...

Two-dimensional (2D) materials have emerged as a significant class of materials promising for photocatalysis, and defect ...

Materials informatics combines data analytics and engineering design, streamlining material development and enhancing performance through AI integration.

A team of researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time -- a development that could lead to the creation of stronger, more ...

Literature searches, simulations, and practical experiments have been part of the materials science toolkit for decades, but the last few years have seen an explosion of machine learning-driven ...

Hydrogen storage is limited by high pressure or cold tanks. Metal hydrides offer efficiency. A large curated database reveals key atomic traits to guide design. (Nanowerk News) Hydrogen fuels ...

Artificial intelligence has made significant strides in dentistry, especially in areas like diagnostics and clinical imaging. However, the application of AI ...