AI predicts extensive material properties to break down a previously insurmountable wall
View list of research
View list of research
AI predicts extensive material properties to break down a previously insurmountable wall
AI predicts extensive material properties to break down a previously insurmountable wall
Researchers from The University of Tokyo Institute of Industrial Science have developed a machine learning model to predict never-before-determined material properties from energy loss near-edge structure (ELNES) and X-ray near-edge structure (XANES) spectra. The spectra of 22,155 organic molecules were input into the neural network model and allowed the first correct prediction of both intensive and extensive material properties. It is hoped that the model will allow high-throughput screening to develop materials across numerous applications.
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