Researchers at #UTokyo_IIS develop a generative AI model that designs crystal and electronic structures together

Researchers from #UTokyo_IIS develop an artificial intelligence framework for designing new inorganic materials with humanlike reasoning

Researchers from #UTokyo_IIS and collaborating partners have created guidelines for the design of intercalated materials, which will accelerate research on energy storage, electronics, and more.

Researchers at #UTokyo_IIS, use artificial intelligence to help interpret data generated by material science spectroscopy experiments, which can aid in the development of new drugs and organic conductors.

Researchers from the The University of Tokyo Institute of Industrial Science use electron energy loss spectroscopy to understand local thermal behavior at grain boundaries in polycrystals

Researchers from The University of Tokyo Institute of Industrial Science report a machine learning-based model for predicting the bonding properties of materials

Scientists at The University of Tokyo study aluminosilicate glass to determine its complex local structure with unprecedented detail. This work may lead to tougher and more inexpensive glass for touchscreens and solar arrays

Scientists at The University of Tokyo use machine learning to predict the excited electronic states of materials—research that can accelerate both the characterization of materials as well as the formulation of new useful compounds

Modern spectroscopy techniques can produce tens of thousands of spectra from a single experiment, which has placed a considerable burden on traditional human-driven methods for interpretation of these spectra. A research team led by The University of Tokyo Institute of Industrial Science combined two machine learning techniques, layer clustering and decision tree methods, to produce data-driven methods for spectral interpretation and prediction that can analyze any spectral data quickly and accurately.