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 ...
Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
Material science, at its core, is an interdisciplinary field focusing on the discovery and design of new materials. It combines elements of physics, chemistry and engineering to understand and ...
Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed ...
Machine learning (ML) enables the accurate and efficient computation of fundamental electronic properties of binary and ternary oxide surfaces, as shown by scientists. Their ML-based model could be ...
(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 ...
By applying machine learning techniques, engineers at MIT have created a new method for 3D printing metal alloys that produce ...
A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy ...
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 ...
Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development.
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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