Developing new materials can involve a dizzying amount of trial and error for different configurations and elements.

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 ...

Materials research generates vast amounts of data, but the information often exists in manufacturer-specific formats and the terminology is inconsistent, making it difficult to aggregate, compare, and ...

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.

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 ...

For the U.S. Army Reserve’s 88th Readiness Division, the best way to effectively manage hazardous materials inventories and provide immediate access to safety data sheets came down to a simple choice: ...

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.

Overview: AI is transforming materials science by dramatically reducing the time needed to discover and test new ...