A new study reveals how machine learning is speeding up solid-state battery development, critical for EVs to gain 50% more range.
Researchers from Skolkovo Institute of Science and Technology and the AIRI Institute have reached a milestone in solid-state battery technology. Their technology could help future electric vehicle s travel up to 50% more on a single charge while simultaneously enhancing safety and battery lifespan.
This breakthrough has been achieved with the application of machine learning to accelerate the discovery of high-performance battery materials. “Improving the energy density, charge rate, and stability of a solid-state battery, the response time of a sensor, or the switching time of a memristor can be achieved by innovations in the architecture of devices and/or material components,” said the researchers in a new study.“Thus, understanding of ionic transport mechanisms and their characteristics is essential for designing advanced ionic conductors.”Neural networks can identify optimal materialsNeural networks have demonstrated the ability to rapidly identify optimal materials for critical solid-state battery components, i.e., the solid electrolyte and its protective coatings.Current EVs largely rely on conventional lithium-ion batteries with liquid electrolytes, which carry a minor fire risk. On the other hand, solid-state batteries utilize solid materials like ceramics to conduct lithium ions, which inherently improves safety and offers superior energy density. Automakers have long sought to integrate this technology, but the absence of suitable solid electrolytes has presented a formidable challenge.“We demonstrated that graph neural networks can identify new solid-state battery materials with high ionic mobility and do it orders of magnitude faster than traditional quantum chemistry methods,” stated Artem Dembitskiy, lead author of the study and a PhD student at Skoltech. “This could speed up the development of new battery materials, as we showed by predicting a number of protective coatings for solid-state battery electrolytes.”Crucial role of protective coatingsThe research also underscores the vital role of protective coatings. These layers are essential for shielding the electrolyte from the highly reactive metallic lithium anode and cathode materials. Without them, battery performance can degrade rapidly, and the risk of short circuits increases. “The metallic lithium of the anode is a strong reducing agent, so almost all existing electrolytes undergo reduction in contact with it. The cathode material is a strong oxidizing agent. When oxidized or reduced, electrolytes lose their structural integrity, which can degrade performance or even cause a short circuit,” explained Assistant Professor Dmitry Aksyonov, a co-author of the study.The machine learning algorithms prove instrumental here. It efficiently screens numerous candidate materials to identify those with ideal properties for durability and efficiency.Practical application for high-performing EVsIn a practical application, the research team successfully utilized their AI-powered methodology to discover novel coating materials for Li10GeP2S12, a leading candidate for solid-state battery electrolytes. Their findings identified promising compounds such as Li3AlF6 and Li2ZnCl4 and paved the way for the development of highly efficient and robust next-generation batteries.This breakthrough can reshape the energy storage landscape with safer, more durable, and higher-performing electric vehicles and portable electronics.
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