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Machine learning is turbocharging cheap lithium-ion battery design
Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has ...
Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for clean-energy reactions are screened, identified, and validated across ...
The field of additive manufacturing is undergoing a profound transformation as artificial intelligence (AI) and machine learning (ML) become integral to the ...
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 ...
Understanding the properties of different materials is an important step in material design. X-ray absorption spectroscopy (XAS) is an important technique for this, as it reveals detailed insights ...
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 ...
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 ...
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 ...
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