Found in knee replacements and bone plates, aircraft components, and catalytic converters, the exceptionally strong metals known as multiple principal element alloys (MPEA) are about to get even ...

Researchers from the Hong Kong University of Science and Technology (HKUST) and Tongji University have developed FerroAI, a deep learning model that can produce phase diagrams for ferroelectric ...

Materials databases lie at the heart of future data-driven discovery in energy-related fields, say researchers from Tohoku ...

The team developed a computational framework using robotic path planning algorithms to rapidly identify optimal composition gradients between dissimilar materials. The framework enables the creation ...

The program will foster “collaboration among faculty from different disciplines,” according to Guarini dean Jon Kull ’88.

This workshop on Autonomous Materials Science will discuss where the weak links are in future systems that will reduce, and eventually eliminate, the need for human intervention in the design and ...

A firefly-inspired AI framework makes atomic structure prediction more robust by combining multimodal search with an uncertainty-aware machine learning technique. The method improves efficiency for ...

In October 2015 the National Academies of Sciences, Engineering, and Medicine convened a workshop of experts from diverse communities to examine predictive theoretical and computational approaches for ...

Materials informatics sits at the intersection of experimental science, computation, and data analytics. The aim is simple: use data and models to make discovering, designing, and deploying new ...