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 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 ...
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 ...
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 ...
Two-dimensional (2D) materials have shown extraordinary potential in electrocatalytic reactions due to their unique structural and electronic properties. In a new review published in AI Mater., first ...
Laser-based metal processing enables the automated and precise production of complex components, whether for the automotive industry or for medicine. However, conventional methods require time- and ...
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 ...
NASA’s Transformational Tools and Technologies project integrates AI, advanced materials, and computational methods to ...
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