For his research in machine learning-based electron density prediction, Michigan Tech researcher Susanta Ghosh has been recognized with one of the National Science Foundation's highest honors. The NSF ...
Machine learning is transforming many scientific fields, including computational materials science. For about two decades, ...
The integration of machine learning techniques into microstructure design and the prediction of material properties has ushered in a transformative era for materials science. By leveraging advanced ...
Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed ...
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 ...
From 11.5 million alloy candidates to AI-guided perovskites, this piece unpacks how materials informatics is speeding up ...
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 ...
More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...
Researchers developed Bits2Bonds, a platform that merges molecular dynamics simulations with machine learning to design new ...
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