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Solid state battery electrolyte enables sodium and lithium battery

Solid state battery electrolyte enables sodium and lithium battery

Technology News |
By Nick Flaherty

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Researchers in the US have used AI to develop an electrolyte for solid state battery cells that allows both sodium and lithium to be used, reducing the reliance on lithium.

The solid state battery material developed at the Pacific Northwest National Laboratory (PNNL) came out of a collaboration using Microsoft’s Azure Quantum Elements to winnow 32 million potential inorganic materials to 18 promising candidates that could be used in battery development in just 80 hours.

It was thought that sodium ions and lithium ions couldn’t be used together in a solid state electrolyte system because they are similarly charged but have different sizes, says PNNL. It was also assumed that the structural framework of a solid state electrolyte material couldn’t support the movement of two different ions. But after testing, “we found that the sodium and lithium ions seem to help each other,” said Vijay Murugesan, materials sciences group lead at PNNL.

The new material has a bonus because its molecular structure naturally has built-in channels that help both ions move through the electrolyte. It has been successfully synthesized and turned into prototype batteries that are functional and will undergo multiple tests in the lab.

Researchers at the Argonne National Laboratory have already used a combination of sodium cathode and lithium for the Ultium battery from General Motors, but with a liquid electrolyte.

Murugesan is looking at the development of a digital twin for chemistry or materials, “so you don’t need to go to a lab and put this material together and make a battery and test it. You can say, ‘this is my anode and this is my cathode and that’s the electrolyte and this is how much voltage I’m going to apply,’ and then it can predict how everything will work together. Even details like, after 10,000 cycles and five years of usage, the material performance will be like this.”

IN identifying the solid state battery electrolyte material, Microsoft first trained different AI systems to do sophisticated evaluations of all the workable elements and to suggest combinations. The algorithm proposed 32 million candidates – like finding a needle in a haystack. Next, the AI system found all the materials that were stable. Another AI tool filtered out candidate molecules based on their reactivity, and another based on their potential to conduct energy.

Azure Quantum Elements offers a cloud computing system designed for chemistry and materials science research with an eye toward eventual quantum computing, and is already working on these kinds of models, tools and workflows. These models will be improved for future quantum computers, but they are already proving useful for advancing scientific discovery using traditional computers.

“At every step of the simulation where I had to run a quantum chemistry calculation, instead I’m calling the machine learning model. So I still get the insight and the detailed observations that come from running the simulation, but the simulation can be up to half a million times faster,” says Nathan Baker, Product Leader for Azure Quantum Elements.

The next set of filters used HPC, which provides high accuracy but uses a lot of computing power. That makes it a good tool for a smaller set of candidate materials. The first HPC verification used density functional theory to calculate the energy of each material relative to all the other states it could be in. Then came molecular dynamics simulations that combined AI and HPC to analyze the movements of atoms and molecules inside each material.

This process culled the list to 150 candidates. Finally, Microsoft scientists used HPC to evaluate the practicality of each material – availability, cost and such – to trim the list to 23 – five of which were already known.

www.pnnl.gov

 

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