Accelerating Battery Innovation: How Neural Networks Are Powering the Future of Solid-State Batteries

Neural networks accelerate battery discovery

The promise of safer, longer-lasting electric vehicles (EVs) is one step closer to reality, thanks to a breakthrough in computational materials science. Researchers at Skoltech and the AIRI Institute have successfully applied neural networks to accelerate the search for next-generation solid-state battery materials—drastically reducing the time and computational cost involved.

Original article: TechXplore, June 5, 2025

The Need for Solid-State Batteries

Conventional lithium-ion batteries power today’s EVs, smartphones, and laptops. But they come with serious drawbacks: limited lifespan, overheating risks, and potential fire hazards. Solid-state batteries (SSBs) are a promising alternative. Instead of using flammable liquid electrolytes, they employ solid materials—often ceramics—to conduct lithium ions between electrodes. This swap can lead to higher energy density, better safety, and extended driving ranges for EVs.

However, despite years of research, a commercially viable solid-state battery has yet to be realized. One major hurdle is finding solid electrolyte materials that tick all the boxes: high ionic conductivity, chemical stability, compatibility with electrodes, and scalability for manufacturing.

Enter Neural Networks: A New Era of Materials Discovery

Traditionally, identifying suitable battery materials involves running thousands of computationally expensive simulations, using methods like density functional theory (DFT). But now, researchers led by Artem Dembitskiy at Skoltech have shown that graph neural networks (GNNs) can dramatically speed up this process.

These machine learning models were trained to predict key material properties—especially ionic conductivity, one of the most crucial and complex traits to evaluate. Once trained, the models could screen vast materials databases orders of magnitude faster than traditional methods, drastically narrowing the search for viable candidates.

Targeting Protective Coatings for Better Battery Performance

One key insight of the study was focusing not just on the electrolyte itself, but also on its protective coatings. In solid-state batteries, both the lithium metal anode and the cathode are highly reactive. Without stable interfaces, reactions can degrade the electrolyte, reduce efficiency, and even cause short circuits.

Dmitry Aksyonov, co-author and assistant professor at Skoltech, explains: "The metallic lithium anode is a strong reducing agent, and the cathode is a strong oxidizer. Protective coatings that are stable on both ends are essential for long-term battery reliability."

The team applied their GNN-powered model to screen for protective materials compatible with Li10GeP2S12 (LGPS), one of the most promising solid-state electrolytes. Their approach identified several strong candidates, including Li3AlF6 and Li2ZnCl4, offering both stability and high ionic mobility.

Implications for the EV Industry

The impact of this research is profound. By compressing years of trial-and-error experimentation into a few days or weeks of intelligent screening, machine learning opens a faster route to commercially viable solid-state batteries. With carmakers racing to outpace each other in EV innovation, these advances could redefine how quickly new energy storage solutions hit the market.

Additionally, this approach could be extended to other components in battery design—from electrodes to separators—enabling a comprehensive, data-driven transformation of battery engineering.

Behind the Breakthrough

Lead author Artem D. Dembitskiy is a Ph.D. student in Skoltech’s Materials Science and Engineering program, as well as a junior researcher at the AIRI Institute. Along with Aksyonov and colleagues, Dembitskiy co-authored the study published in npj Computational Materials, a respected journal covering advances in computational methods for materials discovery.

Reference: A.D. Dembitskiy et al., “Benchmarking machine learning models for predicting lithium ion migration,” npj Computational Materials (2025). DOI: 10.1038/s41524-025-01571-z

Conclusion

As the world shifts toward sustainable energy solutions, battery innovation remains a critical challenge. The fusion of artificial intelligence with materials science—as demonstrated by this study—could be the catalyst for achieving safer, longer-lasting, and higher-performing energy storage systems.

This research highlights the growing importance of interdisciplinary collaboration, where physics, chemistry, and computer science converge to solve one of the 21st century’s most urgent technological problems.

Tags: Solid-State Batteries, Machine Learning, Neural Networks, Electric Vehicles, Battery Materials, Ionic Conductivity, Graph Neural Networks, Materials Discovery, AI in Chemistry, Energy Storage

#SolidStateBattery #NeuralNetworks #BatteryTech #ElectricVehicles #MaterialsScience #GraphNeuralNetwork #EnergyStorage #AIinScience #TechXplore #Skoltech

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