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

Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...

A machine learning method developed by researchers from the Institute of Science Tokyo, the Institute of Statistical Mathematics, and other institutions accurately predicts liquid crystallinity of ...

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 ...

Northwestern Engineering’s Chris Wolverton has been named a fellow of the Materials Research Society for his pioneering work in computational materials science for materials design and discovery, ...

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 ...

Open Materials 2024 will be one of the biggest data sets available for materials science. Meta is releasing a massive data set and models, called Open Materials 2024, that could help scientists use AI ...

In a recent webinar organized by the National Academy of Engineering in connection with its forthcoming Fall 2025 Bridge issue, Rigoberto “Gobet” Advincula, Oak Ridge National Laboratory–University of ...

Chris Van de Walle, a distinguished professor of materials at UC Santa Barbara, has been awarded the American Physical Society’s 2025 Aneesur Rahman Prize for Computational Physics, the highest honor ...

The Arkansas Integrative Metabolic Research Center will host Dr. Prateek Verma, manager of the AIMRC Data Science Core, Wednesday, April 1, to highlight the unique complex-analysis and ...