Science and Technology of Advanced Materials: Methods

Two key challenges in chemistry innovation are solved simultaneously by exploring chemical opportunities with artificial intelligence.

Machine learning algorithms allow analysis and characterization of the atomic arrangement of silicon surface superstructures without the need for human expertise.

A model that rapidly searches through large amounts of materials could find sustainable alternatives to existing composites.

Machine learning and robotic process automation combine to speed up and simplify a process used to determine crystal structures.

Scientists in Japan have combined two computational models to extract more data on steel alloys from a single test, with implications for the discovery of new materials.

A model that rapidly searches through large numbers of materials could find sustainable alternatives to existing composites.

A quick, cost-effective approach improves the accuracy with which machine learning models can predict the properties of new materials.

A quick, cost-effective approach improves the accuracy with which machine learning models can predict the properties of new materials.