Research authored by partners from the Bottle Consortium and published in Nature Communications this month aims to challenge ...
The development of next-generation metallic materials is entering a transformative era driven by data-driven methodologies. Traditional trial-and-error ...
(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 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 ...
Advances in machine learning and shape-memory polymers are enabling engineers to design for mechanical performance first and ...
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 ...
With additive manufacturing, used to build this heat exchanger, one key is to do prototyping for the part and then see whether additive or subtractive manufacturing is the better choice of manufacture ...
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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