Jupyter notebook tuorials/example guides on using Bayesian optimization for voltammetry.
The full data set for the corresponding publication can be found here: https://doi.org/10.5281/zenodo.15339008
See: Movassaghi C, Perrotta K, Curry M, Nashner A, Nguyen K, Wesely M, Alcañiz M, Liu C, Meyer A, Andrews A. Machine-learning-guided design of electroanalytical pulse waveforms.
Includes use of Scikit-Optimize, Ax/BoTorch packages.
SOO = single objective optimization, MOO = multi-objective optimization (under development)
AC BO = Acceleration Consortium Bayesian Optimization hackathon entries (see below).
Video tutorials: