Artificial neural networks—algorithms inspired by connections in the brain—have “learned” to perform a variety of tasks, from pedestrian detection 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. The team details the work in a paper published Sept. 18 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,” noted first author Weike Ye, a chemistry Ph.D. student in Ong’s Materials Virtual Lab.
The team has made their models publicly accessible via a web application at http://crystals.ai. This allows other people to use these neural networks to compute the formation energy of any garnet or perovskite composition on the fly.
The researchers are planning to extend the application of neural networks to other crystal prototypes as well as other material properties.
The Latest on: Artificial neural network
via Google News
The Latest on: Artificial neural network
- Artificial intelligence has the potential to identify the right pharmaceutical component in drug developmenton January 20, 2021 at 1:29 am
Artificial intelligence in drug development in helping the pharmaceutical industry on a massive scale. AI platforms, big data and deep neural networks fast track the drug discovery process.
- Who needs a teacher? Artificial intelligence designs lesson plans for itselfon January 19, 2021 at 12:46 pm
Unlike human students, computers don’t seem to get bored or frustrated when a lesson is too easy or too hard. But just like humans, they do better when a lesson plan is “just right” for their level of ...
- Artificial Neural Networks as a Way to Predict Future Kidney Cancer Incidence in the United States - Beyond the Abstracton January 19, 2021 at 10:45 am
The incidence of kidney cancer is increasing and it could be counteracted with new ways to predict and detect it. Our work has the aim to implement an artificial neural network in order to predict the ...
- Artificial Intelligence Can Beat Many Of Us In Chess, Yet Strangely Not In Memoryon January 19, 2021 at 6:43 am
Computers are well-known for being able to recover information quickly - a Google search will often give you the result you wanted as you type, even if you make spelling errors - but are not known for ...
- Artificial intelligence beats us in chess, but not in memoryon January 15, 2021 at 8:33 am
In the last decades, artificial intelligence has shown to be very good at achieving exceptional goals in several fields. Chess is one of them: in 1996, for the first time, the computer Deep Blue beat ...
- Using neural networks for faster X-ray imagingon January 13, 2021 at 4:38 am
A team of scientists from Argonne is using artificial intelligence to decode X-ray images faster, which could aid innovations in medicine, materials and energy.
- What this bald eagle and neural network depiction have to do with future U.S. AI strategyon January 12, 2021 at 11:03 pm
The White House launched the National AI Initiatives Office, a group with an eagle and neural network on its seal, to plan U.S. AI strategy.
- A new technique called ‘concept whitening’ promises to provide neural network interpretabilityon January 12, 2021 at 2:01 pm
"Concept whitening" can help steer neural networks toward learning specific concepts without sacrificing performance.
- Can neural networks experience bizarre hallucinations like us?on January 8, 2021 at 9:11 am
While Google’s neural network created images grabbed worldwide attention, we must fine tune these artificial intelligence models to generate perfect deepfake and visual content, by understanding how ...
via Bing News