A massive new database of dielectric material properties could speed up the development of electronics like smartphones and energy storage systems. AI-driven materials discovery has great potential to ...

In data-driven research, the most crucial resource is data. However, compared to AI-advanced fields such as natural language processing, computer vision, biology, and medicine, the data resources in ...

Technologies that underpin modern society, such as smartphones and automobiles, rely on a diverse range of functional materials. Materials scientists are therefore working to develop and improve new ...

Data-driven science represents a transformative paradigm in materials science. Both data-driven materials science and informatics encompass systematic knowledge extraction from materials datasets.

Nanoengineers have developed an AI algorithm that predicts the structure and dynamic properties of any material -- whether existing or new -- almost instantaneously. Known as M3GNet, the algorithm was ...

The National Institute for Materials Science (NIMS) has been developing the DICE materials data platform* 1 to fulfill its role as a data core center in the MEXT-led project to develop materials DX ...

Waste analytics company Greyparrot has launched Deepnest, an artificial intelligence- (AI-) powered waste intelligence platform designed to give brands direct access to their recyclable material data.