AI in Battery Research: How to Spot Hype vs Real Insight

AI in Battery Research: How to Spot Hype vs Real Insight
AI is powerful—but in battery R&D, overclaim is becoming common. A few quick checks I use as a reviewer:
• Claims must match data: coin cells ≠ EV packs
• Cells matter, not cycles: many cycles from few cells is a red flag
• Physics first: LLI (Loss of Lithium Inventory), LAM (Loss of Active Material), SEI, plating must be identified before ML
• Meaningful features: dQ/dV, EIS, CE > raw voltage curves
• True prediction: early-cycle data → future failure (not hindsight)
• Show failures & uncertainty: batteries are path-dependent
• AI supports understanding, not replaces electrochemistry
Rule of thumb:
If AI speaks louder than battery physics, it’s probably hype.
Healthy skepticism helps keep AI useful—not fashionable—in energy storage research.