This is a schematic of an artificial neural network predicting a stable garnet crystal prototype. Image: Weike Ye.
This is a schematic of an artificial neural network predicting a stable garnet crystal prototype. Image: Weike Ye.

Artificial neural networks – algorithms designed to replicate the connections between neurons in the brain – have ‘learned’ to perform a variety of tasks, from detecting pedestrians in self-driving cars, to analyzing medical images, to translating languages. Now, researchers at the University of California San Diego are training artificial neural networks to predict new stable materials.

"Predicting the stability of materials is a central problem in materials science, physics and chemistry," said senior author Shyue Ping Ong, a nanoengineering professor at the UC San Diego Jacobs School of Engineering. "On one hand, you have traditional chemical intuition such as Linus Pauling's five rules that describe stability for crystals in terms of the radii and packing of ions. On the other, you have expensive quantum mechanical computations to calculate the energy gained from forming a crystal that have to be done on supercomputers. What we have done is to use artificial neural networks to bridge these two worlds."

By training artificial neural networks to predict a crystal's formation energy using just two inputs – electronegativity and ionic radius of the constituent atoms – Ong and his team at the Materials Virtual Lab have developed models that can identify stable materials in two classes of crystals known as garnets and perovskites. These models are up to 10 times more accurate than previous machine learning models and are fast enough to efficiently screen thousands of materials in a matter of hours on a laptop. Ong and his team detail their work in a paper in Nature Communications.

"Garnets and perovskites are used in LED lights, rechargeable lithium-ion batteries, and solar cells. These neural networks have the potential to greatly accelerate the discovery of new materials for these and other important applications," said first author Weike Ye, a chemistry PhD student in the Materials Virtual Lab.

The team has made its models publicly accessible via a web application at, thus allowing other people to use these neural networks to compute the formation energy of any garnet or perovskite composition on the fly. The researchers are now planning to apply neural networks to other crystal prototypes as well as other material properties.

This story is adapted from material from the University of California San Diego, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.