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 ...
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?
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 ...
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 ...
Metalworkers and metallurgists have long appreciated the ability to tailor the performance characteristics of steel (an alloy of iron and carbon), including their strength, hardness, ductility and ...
Although occupation of dodecahedral cages by TBAB had not been previously reported, it was found that utilizing dodecahedral cages allows for higher packing density of TBAB. The results were shared in ...
Crystals -- from sugar and table salt to snowflakes and diamonds -- don't always grow in a straightforward way. Researchers have now captured this journey from amorphous blob to orderly structures. In ...
Duplicates of crystal structures are flooding databases, implicating repositories hosting organic, inorganic, and computer-generated crystals. The issue raises questions about curation practices at ...
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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 ...
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