The method identifies magnetic materials solely based on their crystal structure, eliminating the need for time-consuming experiments and simulations
Single-molecule magnets (SMMs) are exciting materials. In a recent breakthrough, researchers from Tokyo University of Science have used deep learning to predict SMMs from 20,000 metal complexes.
The predictions were made solely based on the crystal structures of these metal complexes, thus eliminating the need for time-consuming experiments and complex simulations.
As a result, this method is expected to accelerate the development of functional materials, especially for high-density memory and quantum computing devices. [Read more…] about Tokyo university finds way to use deep learning to predict ‘exciting materials’
