(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 ...
Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed ...
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 ...
Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development.
A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy ...
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 ...
Using AI and machine learning as transformative solutions for semiconductor device modeling and parameter extraction.
Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...
Scientists have used artificial intelligence (AI) to design never-before-seen nanomaterials with the strength of carbon steel and the lightness of styrofoam. The new nanomaterials, made using machine ...
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 ...
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