AI Unveils Chemistry of High-Performance Battery Electrolytes

Cornell researchers develop AI framework to predict and explain battery electrolyte performance

Published on Feb. 27, 2026

Got story updates? Submit your updates here. ›

A new artificial intelligence framework developed at Cornell can accurately predict the performance of battery electrolytes while revealing the chemical principles that govern them, providing engineers with a new tool for designing better batteries. The framework focuses on high-performing lithium-ion batteries that use nonaqueous electrolytes and uses AI to predict how salts, solvents and operating conditions work together to enable ion transport.

Why it matters

Battery chemistry involves many coupled variables, and understanding how they interact is essential for rational design. This new AI-powered framework can improve prediction of electrolyte performance while also providing insight into the underlying chemistry, which is critical for building reliable and scalable design tools for next-generation batteries.

The details

The framework, published in Nature Computational Science, treats salts, solvents and operating conditions as distinct but interacting contributors to conductivity, processing chemically meaningful descriptors for those three components separately and then adaptively integrating them. When applied to a large experimental dataset of lithium-ion electrolytes, the framework reduced prediction error by more than 65% compared with leading machine-learning methods and remained accurate across the full conductivity range, including rare high-conductivity formulations.

  • The framework was published on February 19, 2026.

The players

Fengqi You

The Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering at the Cornell Duffield College of Engineering, and a senior faculty fellow at the Cornell Atkinson Center for Sustainability.

Zhilong Wang

A postdoctoral researcher who is the first author of the study.

Cornell AI4S Initiative

An ongoing effort at Cornell to bring together faculty and students from across campus to apply AI to energy, materials and environmental challenges.

Got photos? Submit your photos here. ›

What they’re saying

“Battery chemistry involves many coupled variables, and understanding how they interact is essential for rational design. We are developing AI tools that improve prediction while also providing insight into the underlying chemistry.”

— Fengqi You, The Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering at the Cornell Duffield College of Engineering (Mirage News)

“For energy materials, it is not enough to rely on black-box predictions. Interpretability and integration with physics are critical for building reliable and scalable design tools.”

— Fengqi You, The Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering at the Cornell Duffield College of Engineering (Mirage News)

What’s next

The researchers plan to continue developing the AI framework and applying it to other energy materials challenges, with the goal of creating reliable and scalable design tools for next-generation batteries.

The takeaway

This new AI-powered framework represents a significant advancement in battery research, as it can not only accurately predict electrolyte performance, but also provide valuable insights into the underlying chemistry. By integrating AI with physics-based models, the researchers are paving the way for more efficient and effective battery design, which is crucial for the development of high-performance energy storage solutions.