/* ---- Google Analytics Code Below */

Thursday, December 01, 2022

Predictive Database for Materials

Materials science becoming increasingly important

 Nanoengineers Develop Predictive Database for Materials

UC San Diego Today, Emerson Dameron, November 28, 2022

The M3GNet algorithm developed by nanoengineers at the University of California, San Diego (UCSD)'s Jacobs School of Engineering can forecast the structure and dynamic properties of any material almost instantaneously. Researchers used M3GNet to compile the matterverse.ai database of more than than 31 million yet-to-be-synthesized materials with traits predicted by machine learning algorithms. UCSD's Shyue Ping Ong and colleagues combined graph neural networks with many-body interactions into a highly accurate deep learning framework that operates across the entire periodic table. The team employed the Materials Project's database of materials energies, forces, and stresses to train the predictive M3GNet interatomic potential model. "We truly believe that the M3GNet architecture is a transformative tool that can greatly expand our ability to explore new material chemistries and structures," said Ong.

Full Article    

No comments: