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 ...
Many high-performance HEAs used in areas such as aerospace engines, gas turbines, and nuclear power plants employ CRMs such as tantalum, niobium, tungsten, and hafnium. These elements are expensive, ...
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IIT-G develops ML method to design advanced alloys without critical raw materials
IIT Guwahati researchers, with UK collaborators, have used Machine Learning to design advanced, high-performance metal alloys without relying on Critical Raw Materials (CRMs), creating a sustainable ...
Overview: AI is transforming materials science by dramatically reducing the time needed to discover and test new ...
Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
You’ll tackle projects in computational materials design (from high-throughput modeling and phase-diagram simulations to training machine-learning models on experimental signals such as UV–Vis/IR) ...
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 ...
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