Organic molecular crystals can respond to external stimuli such as heat, light, and mechanical force, making them attractive candidates for next-generation functional materials. However, predicting ...

The ability to predict crystal structures is a key part of the design of new materials. New research shows that a mathematical algorithm can guarantee to predict the structure of any material just ...

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 ...

For decades, chemists have relied on boron-bound nitrenes as fleeting intermediates in synthesis, but no one has been able to ...

A software workflow automates X-ray analysis to spot crystal defects in diamond and advanced semiconductors, helping improve ...

Using artificial intelligence to create new things is all the rage right now. Whether you want text, computer code, or images, there are uncountable generative AI models that can oblige. Google ...

Google DeepMind researchers have discovered 2.2 million crystal structures that open potential progress in fields from renewable energy to advanced computation, and show the power of artificial ...

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 ...

Duplicates of crystal structures are flooding databases, implicating repositories hosting organic, inorganic, and computer-generated crystals. The issue raises questions about curation practices at ...

TRUNNANO (Luoyang Tongrun Nano Technology Co., Ltd.), a global leader in nanotechnology and advanced materials development, today announced the official launch of its new Battery Materials Division.