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 ...
Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed ...
Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development.
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 ...
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 ...
A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy ...
Using AI and machine learning as transformative solutions for semiconductor device modeling and parameter extraction.
In this interview, Markus Buehler discusses MIT's Machine Learning for Materials Informatics course, its curriculum, and its impact on the broader materials science community, emphasizing the exciting ...
A recent study published in Small highlights how machine learning (ML) is reshaping the search for sustainable energy materials. Researchers introduced OptiMate, a graph attention network designed to ...
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