X-ray diffraction (XRD) is one of the most widely used structural characterization techniques for inorganic crystalline materials. Since Bragg’s law was first proposed in 1912, X-ray crystallography ...
Generative artificial-intelligence (AI) models, such as score-based diffusion models, have recently advanced the field of computational materials science by enabling the generation of new materials ...
Researchers at New York University have devised a mathematical approach to predict the structures of crystals—a critical step in developing many medicines and electronic devices—in a matter of hours ...
A research team from the Institute of Statistical Mathematics and Panasonic Holdings Corporation has developed a machine learning algorithm, ShotgunCSP, that enables fast and accurate prediction of ...
On the trail of the missing hydrogen atoms. Villigen, 11.06.2026 — To simulate the properties of materials, researchers use crystal stru ...
The new method can determine crystal structures underlying experimental data thus far difficult to analyze. A joint research team led by Yuuki Kubo and Shiji Tsuneyuki of the University of Tokyo has ...
A new artificial intelligence model can predict how atoms arrange themselves in crystal structures. A new artificial intelligence model that can predict how atoms arrange themselves in crystal ...
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 ...
UB chemist Jason Benedict and his team spent years developing photoswitchable crystals. Every crystal’s shape is a mirror of the internal arrangement of their molecules, but the molecules in ...
An artificial intelligence created by Google DeepMind may help revolutionise materials science, providing new ways to make better batteries, solar panels, computer chips and many more vital ...
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