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Stanford Scientists Combine AI, Atomic-Scale Images in Pursuit of Better Batteries

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Artists rendition of a particle analyzed by a combination of machine learning, X-ray and electron microscopy.

Using artificial intelligence for image analysis is not new, but using it to study atomic interactions at the smallest of scales is.

Credit: Ella Maru Studio

A research team led by Stanford University analyzed atomic-scale microscopic images using artificial intelligence (AI) to understand why rechargeable batteries wear out, paving the way for better batteries in the future.

The researchers studied a type of lithium-ion battery based on lithium ion phosphate (LFP) materials.

They leveraged AI to study the atomic interactions in the materials at the single nanometer scale.

Applying image-learning techniques to two-dimensional images produced by a scanning transmission electron microscope and to advanced X-ray images, the researchers were able to understand the elasticity and deformation of the material as it charges and discharges and how it expands and contracts where the LFP is partially stable.

Said Stanford's Haitao "Dean" Deng, "AI can help us understand these physical relationships that are key to predicting how a new battery will perform, how dependable it will be in real-world use, and how the material degrades over time."

From Stanford News
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Abstracts Copyright © 2022 SmithBucklin, Washington, DC, USA


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