I'm sorry that you found it terse. I intended this as a brief intro and would encourage people who found it a little bit interesting or relevant to their problems to either read the more extensive examples on PyBaMM. If of interest, Silicon anodes are only one type of material being extensively researched in order to improve batteries. The holy grail is Lithium metal where there is no host material on the anode side at all. This would give the ultimate energy density but has so far largely eluded commercialization.

> > experiment = pybamm.Experiment( [ ( "Discharge at C/10 for 10 hours or until 3.3 V", "Rest for 1 hour", "Charge at 1 A until 4.1 V", "Hold at 4.1 V until 50 mA", "Rest for 1 hour", ) ] * 3, )

Wait, does this thing take natural language instructions? Does an LLM parse this to something more stuctured?

"Charge at 1C for 1 hour, place under campfire, discharge at 1000 C, cook marshmallows"

Hahaha. No marshmallow instructions for us yet but some people are using PyBaMM to model thermal runaway. We don't use an LLM but I guess could in order to catch more edge cases. There is an experiment class and a step class. Each step can be called with arguments or passed a string that equates to the same thing but you will get errors if you mistype or leave something out. https://docs.pybamm.org/en/v23.5_a/source/api/experiment/exp...

Ah so it's more of a rigorously formatted code that looks like natural language, COBOL style :P

Figured it's more likely that, interesting choice for sure.

PyBaMM predates ChatGPT. But we're emulating cyclers which are fairly basic tbh. I'm exploring ways of using LLMs to improve the experience. ie Cycle my NMC-Gr 18650 with a CCCV at 1C with an RPT cycle of CCCV and GITT at C/10 every 10 cycles until 80% capacity using model options x,y,z