Deep-learning system explores materials’ interiors from the outside
Maybe you can’t tell a book from its cover, but according to researchers at MIT you may now be able to do the equivalent for materials of all sorts, from an airplane part to a medical implant. Their new approach allows engineers to figure out what’s going on inside simply by observing properties of the material’s surface.
We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.
Researchers at the University of New Hampshire have harnessed artificial intelligence to accelerate the discovery of new functional magnetic materials, creating a searchable database of 67,573 magnetic materials, including 25 previously unrecognized compounds that remain magnetic even at high temperatures.
Advanced materials become increasingly complex due to the high requirements they have to fulfill regarding sustainability and applicability. Dierk Raabe and colleagues reviewed the use of artificial intelligence in materials science and the untapped spaces it opens if combined with physics-based simulations. Compared to traditional simulation methods AI has several…