(Nanowerk Spotlight) The integration of machine learning (ML) into materials science is rapidly transforming the way researchers discover and design advanced ...
Identifying optimal catalyst materials for specific reactions is crucial to advance energy storage technologies and sustainable chemical processes. To screen catalysts, scientists must understand ...
A perspective highlights the transformative impact of machine learning (ML) on enhancing carbon dioxide reduction reactions (CO 2 RR), steering us closer to carbon neutrality. It emphasizes ML's ...
Researchers from the University of Houston are using AI and microwave plasma to improve catalysts for renewable energy processes. They aim to accelerate catalyst discovery and enhance efficiency in ...
The conversion of carbon dioxide into clean fuels is regarded as an important route toward carbon neutrality. CO 2 methanation, in particular, has drawn increasing interest due to its favorable ...
Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback