Materials testing is critical in product development and manufacturing across various industries. It ensures that products can withstand tough conditions in their ...
(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 ...
Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for ...
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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 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 key advantage of the approach lies in its ability to jointly address mechanical resilience and electrical functionality. Li’s group found that by ...
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 ...
The field of additive manufacturing is undergoing a profound transformation as artificial intelligence (AI) and machine learning (ML) become integral to the ...
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 ...
How additive manufacturing advanced the development of functionally graded materials. Why compositionally graded materials present a greater challenge to materials engineers. How computational ...
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