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?
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Crystallography-informed AI achieves high performance in predicting novel crystal structures
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 ...
Every crystal's shape is a mirror of the internal arrangement of its molecules, but the molecules in photoswitchable crystals can expand, twist and change properties—from their color to their ...
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 ...
Duplicates of crystal structures are flooding databases, implicating repositories hosting organic, inorganic, and computer-generated crystals. The issue raises questions about curation practices at ...
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 ...
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 ...
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