Today’s rechargeable batteries are a wonder, but far from perfect. Eventually, they all wear out, begetting expensive replacements and recycling.

“But what if batteries were indestructible?” asks William Chueh, an associate professor of materials science and engineering at Stanford University and senior author of a new paper in Nature Materials that details a first-of-its-kind analytical approach to building better batteries.

Chueh, together with lead author Haitao ‘Dean’ Deng and collaborators at Lawrence Berkeley National Laboratory, MIT and other research institutions, used artificial intelligence (AI) to analyze new kinds of atomic-scale microscopic images to understand exactly why batteries wear out. Eventually, the researchers say, these revelations could lead to batteries that last much longer than those of today. Specifically, they looked at a particular type of lithium-ion battery based on so-called LFP materials, which don’t use materials with constrained supply chains.

“Think of a battery as a ceramic coffee cup that expands and contracts when it heats up and cools off. Those changes eventually lead to flaws in the ceramic,” Chueh explained. “The materials in a rechargeable battery do the same each time you recharge it and then use up that electricity, leading to failure.”

In the battery, Chueh noted, it is not temperature that causes the fissures, but the mechanical strain the materials put on one another with each charge cycle.

“Unfortunately, we don’t know much about what’s happening at the nanoscale where atoms bond,” Chueh said. “These new high-resolution microscopy techniques allow us to see it and AI helps us understand what is happening. For the first time, we can visualize and measure these forces at the single nanometer scale.”

According to Chueh, the performance of any given material is a function of both its chemistry and the physical interaction in the material at the atomic scale – what he refers to as ‘chemo-mechanics’. What’s more, the smaller things get and the more diverse the atoms making up the material are, the harder it is to predict how the material will behave, which is why the researchers turned to AI.

Using AI for image analysis is not new, but using it to study atomic interactions at the smallest of scales is. In medicine, AI has become a transformative tool in analyzing images of everything from faulty knees to deadly cancers. Meanwhile, in materials science, new methods of high-resolution X-ray, electron and neutron microscopy are allowing direct visualization at the nanoscale.

For their subject, the team chose lithium iron phosphate (LFP), a well-known material used in positive electrodes that is gaining popularity with electric car makers and other battery-intensive businesses. This electrode does not contain cobalt and nickel, which are used in many commercially available batteries. LFP batteries are also safer, though they hold less electricity per pound.

Although LFP has been studied for two decades, two key outstanding technical questions could only be guessed at until now. The first involves understanding the elasticity and deformation of the material as it charges and discharges. The second pertains to how it expands and contracts in a specific regime where the LFP is partially stable, or ‘metastable’.

Deng helped explain both for the first time using his image-learning techniques, which he applied to a series of two-dimensional images produced by a scanning transmission electron microscope and to advanced (spectro-ptychography) X-ray images. The findings, he said, are important for understanding a battery’s capacity, energy retention and rate. Better yet, he thinks the findings are generalizable to most crystalline materials that might also make good electrodes.

“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,” Deng said.

Chueh calls Deng an ‘academic entrepreneur’. He is a chemist by background but taught himself the nuances of AI to take on this challenge. Deng described this approach as a form of ‘inverse learning’, in which the result is known – high-resolution still images of degraded LFP – and AI helps reconstruct the physics to explain how it got that way. That new knowledge, in turn, becomes the basis for improving the materials.

Deng noted that previous non-AI studies have illuminated correlations in how mechanical stresses affect electrode durability, but this new approach provides both an exciting way and the motivation to develop a more fundamental understanding of the mechanics at play.

Next up, the researchers say they are already at work on applying their techniques to elucidate promising new battery designs at the atomic level. One outcome might be new battery control software that manages charging and discharging in ways that can improve battery life. Another exciting avenue is the development of more accurate computational models that allow battery engineers to explore alternative electrode materials on a computer instead of in a lab.

“That work is already underway,” Chueh said. “AI can help us look at old materials in new ways and maybe identify some promising alternatives from some as-yet-unknown materials.”

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

Artist’s rendition of a particle analyzed by a combination of AI, and X-ray and electron microscopy. Image: Ella Maru Studio.
Artist’s rendition of a particle analyzed by a combination of AI, and X-ray and electron microscopy. Image: Ella Maru Studio.