Developing and manufacturing effective, safe, reliable new drugs or critical new materials for use in semiconductors or applications involving dangerous materials requires many layers of knowledge.

When scientists study how materials behave under extreme conditions, they typically examine what happens under compression. But what occurs when you pull matter apart in all directions simultaneously?

“Crystal Math” uses equations—and minimal resources—to rapidly predict the 3D structures of molecular crystals, which could speed up R&D for drugs and electronic devices Researchers at New York ...

SPaDe-CSP first predicts most probable space groups and crystal densities using machine learning and then employs an efficient neural network potential for structure refinement. Prediction of crystal ...

BUFFALO, N.Y. — University at Buffalo chemist Jason Benedict and his team spent years developing photoswitchable crystals. Every crystal’s shape is a mirror of the internal arrangement of their ...

MIT physicists have synthesized a new family of bulk crystals whose electrons behave as though they move in an effectively four-dimensional “superspace,” rather than being fully described by the three ...