SeroOpt

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:

• https://ac-bo-hackathon.github.io/projects/project-30-sero-opt/
• https://ac-bo-hackathon.github.io/projects/project-31-ask-tell-tutorial/