A research team led by Prof. Sangwon Seo of the Department of Physics and Chemistry at DGIST has developed a catalytic ...
This study develops an ML-assisted approach for accelerating the discovery of HER electrocatalysts in multi-principal element ...
A high-precision machine learning potential based on the Deep Potential method was constructed to systematically investigate ...
Research shows that a copper(II) complex monolayer on graphene maintains strong antiferromagnetic order, key to advancing ...
Some of the points include eye and cognitive tests for drivers over 70 and learner drivers facing a minimum learning period ...
A new breakthrough is transforming MXenes—ultra-thin, high-tech materials—into something far more powerful and precise.
Morning Overview on MSN
New method predicts how stretching metals shifts catalytic activity
A computational method published in Cell Reports Physical Science offers a transferable way to predict how lattice strain ...
Tech Xplore on MSN
High-entropy design achieves 3-fold increase in hydrogen production
While mixing materials typically leads to instability, there exists a phenomenon known as high entropy, where increasing ...
Discovered in 2011, MXenes are a fast-expanding family of ultra-thin inorganic materials. They are made from stacked layers of transition metals ...
While mixing materials typically leads to instability, there exists a phenomenon known as "high entropy," where increasing compositional complexity ...
Power delivery now spans stacked dies, interposers, bridges, and packages connected by thousands of micro-bumps and TSVs.
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