New contributor interested in ML-ready electrochemical energy-storage data

General
1

Hello everyone,

My name is Alper Bingöl. My research background is in supercapacitor electrode materials, electrochemical energy storage, and machine-learning-assisted materials analysis. I have worked on bio-derived and metal-oxide electrode materials, and I am currently developing reproducible ML workflows for energy-storage data, including thermal descriptors, electrochemical performance data, and prediction pipelines.

I recently joined the Battery Data Alliance community because I am very interested in open battery data standards, BDF, and AI-ready data structures for electrochemical energy-storage research.

I would be especially interested in contributing from the perspective of supercapacitor and electrode-material datasets, including CV, GCD, EIS, electrode composition, electrolyte, mass loading, capacitance, energy density, power density, and thermal/TGA-DTG descriptors.

Could you please suggest the best discussion thread or working group for a new contributor with this background?

2

Welcome Alper, glad to have you here.

Your ML and electrochemistry background fits well with where BDF is heading. The main repo is GitHub - battery-data-alliance/battery-data-format: Battery Data Format Definition · GitHub and we’re closing in on a 0.2.0 release (the milestone there shows what’s in flight). If you’re looking for a place to start, the EIS columns discussion (EIS columns · Issue #4 · battery-data-alliance/battery-data-format · GitHub) could use someone with your background, and we’re always looking for people to test the package against real datasets: pip install batterydf.

Feel free to open issues with anything you hit.