We know of around 48,000 inorganic crystal structures, which provide materials with a range of properties. Now, an AI created by Google DeepMind has predicted over 2 million more possibilities
By Alex Wilkins
29 November 2023
A crystal structure predicted by the GNoME AI. It contains barium (blue), niobium (white) and oxygen (green).
Materials Project/Berkeley Lab
An artificial intelligence created by Google DeepMind may help revolutionise materials science, providing new ways to make better batteries, solar panels, computer chips and many more vital technologies.
“Anytime somebody wants to improve their technology, it inevitably includes improving the materials,” says Ekin Dogus Cubuk at DeepMind. “We just wanted them to have more options.”
The AI model, called Graph Networks for Materials Exploration, or GNoME, is designed to predict inorganic crystal structures, which are repeating arrangements of atoms that provide materials with particular properties – for example, the six-fold symmetry of a snowflake is a result of the crystal structure of ice.
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Organic crystals, which include carbon-hydrogen bonds, are well understood because of numerous examples in biological systems, but until now we only knew of around 48,000 possible inorganic crystals. GNoME has massively expanded that figure to more than 2 million, and while some of these new structures might decay into more stable forms or be impossible to create altogether, more than 700 of the predictions have already been made in the lab.
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GNoME is a graph neural network, a kind of AI that can learn the relationships between objects, such as atoms and their chemical bonds. Cubuk and his team trained GNoME on an existing database of known inorganic crystals and used it to generate new possible crystals by changing the elements or playing with the known crystals’ symmetries. It also predicted the energies of the new crystals, a measure of their stability.