Nanoengineers develop a predictive database for materials. A breakthrough algorithm expands the exploration space for materials by orders of magnitude
the team behind M3GNet, led by UC San Diego nanoengineering professor Shyue Ping Ong, uses matterverse.ai and the new capabilities of M3GNet in their search for safer and more energy-dense electrodes and electrolytes for rechargeable lithium-ion batteries. The project is explored in the Nov. 28 issue of the journal Nature Computational Science . The properties of a material are determined by the arrangement of its atoms. However, existing approaches to obtain that arrangement are either prohibitively expensive or ineffective for many elements. "Similar to proteins, we need to know the structure of a material to predict its properties," said Ong, the associate director of the Sustainable Power and Energy Center at the Jacobs School of Engineering. "What we need is an AlphaFold for materials." AlphaFold is an AI algorithm developed by Google DeepMind to predict protein structure. To build the equivalent for materials, Ong and his team combined graph neural networks w...