Machine Learning Battery Guess – Generated Surfaces? Water Ice Cubes And Energy

How does machine learning make sense to a battery? With machine learning we can make decision in an automatic way. So I wondered if there should exist batteries with multiple outputs.

Maybe tests should be made with machine learning on a simple galvanic cell first.

The idea with the ice in the water photo is that ice act like a energy absorber. Using naive connection logic this would be connected to the electrodes. So should the electrode surfaces also generated in real battery?

If you withdraw energy from different places in a divided electrode. Would that not change its overall surface look.

With multiple outputs from the electrodes it ?should be possible to use a machine learning decision models for those output options. Like adapted watt per connection usage as function many data.