High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations.

Element Materials Technology’s AI-Driven Discovery Finding new materials used to take ages. Seriously, think years of lab ...

(Nanowerk Spotlight) Computational calculations are revolutionizing modern scientific research, offering a powerful means to predict the potential applications of new materials. Unlike traditional ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

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 ...

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) A feasible route for developing large materials models capable of describing the structure-property relationship of materials. The universal materials model of DeepH accepts an arbitrary material ...

How can generative AI help scientists find new material synthesis paths? This is what a recent study published in Nature Computational Science hopes to add | Technology ...

Professor James Rondinelli collaborated with IBM to help turn simulations into potential practical chip designs.

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 ...