Latest News Releases 16 October

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.

Researchers performed the world’s first measurement of how the size of the radium nucleus modifies the structure of molecules containing different radium isotopes. Violations of fundamental symmetries help explain why there is more matter than antimatter in the universe. Radioactive molecules containing isotopes of heavy elements like radium are ideal for studying violation of fundamental symmetries.