Matlantis CSP has already produced early results across multiple systems—oxides, alloys, and phosphides—discovering more than 10 previously unknown stable crystals. In the Ga–Au–Ca system, it ...
Researchers from the labs of Professors Vinayak Dravid and Omar Farha developed a high-resolution approach to map ...
Condensed-matter physics and materials science have a silo problem. Although researchers in these fields have access to vast amounts of data – from experimental records of crystal structures and ...
Hosted on MSN
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 ...
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?
Mid- and far-infrared birefringent crystals are key functional materials for polarization control, laser technologies, and ...
“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 ...
Duplicates of crystal structures are flooding databases, implicating repositories hosting organic, inorganic, and computer-generated crystals. The issue raises questions about curation practices at ...
Rare-earth magnets are essential for electric motors in vehicles, drones, and trains, forming the backbone of modern, ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results