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 ...

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: ...

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 ...

Researchers at Japan’s National Institute for Materials Science have developed two large language model-powered tools to automate extracting experimental materials data from open-access scientific ...

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 complexity problem The most direct explanation for why materials misbehave in production is also the most uncomfortable ...

The Raw Materials Research and Development Council has launched an upgraded national raw materials database designed to ...