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Focused Ion Beam Fabrication of LiPON-based Solid-state Lithium-ion Nanobatteries for In Situ Testing

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Real-time artificial intelligence for solid-state lithium metal batteries.

Mingyang Wang^1,2, Yifan Wu¹, Yuchuang Cao¹

¹John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.

Nature Communications

|December 16, 2025

Summary

This summary is machine-generated.

Machine learning integrated into battery testers detects early failure signals. Reinforcement learning adjusts cycling in real-time, significantly extending battery life and performance.

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Area of Science:

Materials Science
Electrochemistry
Artificial Intelligence

Background:

Battery failure often follows a transition from slow to rapid capacity decay.
Early detection of failure signals allows for preventative adjustments to cycling procedures.

Purpose of the Study:

To integrate machine learning into battery test stations for early failure detection.
To utilize reinforcement learning for real-time adjustment of cycling procedures to enhance battery longevity.

Main Methods:

Developed an integrated machine learning module for electrochemical test stations.
Employed reinforcement learning to dynamically adjust battery cycling parameters.
Utilized solid-state lithium metal batteries for rapid data generation and feedback.

Main Results:

Achieved a 265% improvement in battery lifetime.
Increased accumulative specific energy by 250% at 80% state of health.
Effectively controlled detrimental interface reactions through intelligent control.

Conclusions:

Demonstrated a proof-of-concept for built-in intelligence in battery management systems.
Showcased a pathway for AI-driven real-time battery control for enhanced longevity and safety.
Highlighted the potential to surpass human expertise in battery management.