Researchers have developed a framework that uses machine learning to accelerate the search for new proton-conducting materials, that could potentially improve the efficiency of hydrogen fuel cells.

Single-molecule magnets (SMMs) are exciting materials. In a recent breakthrough, researchers have used deep learning to predict SMMs from 20,000 metal complexes. The predictions were made solely based ...

Researchers claim model can cut years from testing cycles Scientists have developed a machine learning method that could dramatically slash the cost and energy required to develop new lithium-ion ...

The proton-conducting layer currently found in solid oxide fuel cells is typically made from a perovskite structure (left). Using machine learning, a research team, led by Kyushu University, has ...

(Nanowerk News) Researchers at Kyushu University, in collaboration with Osaka University and the Fine Ceramics Center, have developed a framework that uses machine learning to speed up the discovery ...