An illustration of the machine learning model framework, showing its application in predicting the electro-mechanical behavior of CNTs/PDMS composites. As featured in National Science Open, the ...
Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
Artificial Intelligence and its related tools, such as machine learning, deep learning, and neural networks, are revolutionizing every field of life. The domain of materials science and engineering is ...
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 ...
Materials testing is critical in product development and manufacturing across various industries. It ensures that products can withstand tough conditions in their ...
Electro- and photocatalytic materials are central to enabling sustainable energy conversion processes such as water splitting, CO2 reduction, oxygen ...
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 ...
Machine learning, with its ability to analyze large datasets and identify patterns, is particularly well-suited to address the challenges presented by the vast and complex data generated in ...
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