Bayesian Optimization Hackathon for Chemistry and Materials, Project 15: Adaptive Batch Sizes for Bayesian Optimization of Reaction Yield Project overview and goal:

https://github.com/AC-BO-Hackathon/ac-bo-hackathon.github.io/blob/main/_projects/project-15-best_batchers.md

Project challenge in a nutshell:

Quite often, when performing experiments in batches it costs most of the time for the experimentalist in the lab to prepare the first experiment for a batch. Adding more experiments to the same batch takes less effort (all instruments are checked and ready to go, chemist is already in the lab just has to prepare an additional mixture etc.) Considering the “discounted” time of the additional experiments in a batch, and the reduced retraining time when training on multiple samples at once, how do we determine the optimal batch size at each iteration towards maximizing reaction yield in the shortest time?

Eventually: Minimize the time you have to spend in a lab! The details are described in the project proposal (link above)

Some Guidelines to consider

1. This repo is the main repo of our project. If you have your own idea or solution it would be most effective to share it in the discord channel and create your own branch and finally merge it with this main branch.
2. However, Feel free to self organize however you wish!
3. Use the init_data.py script to test your suggested approach. This way we make sure all our suggestions are comparible with each other use the same split and initialization. See details on this script below!
4. Double check the function that tracks the time needed to perform the experiments and fit, this is trivial but critical but benchmarking. Details on time requirements for experiments and fitting below!
5. Have fun! :)

INSTALL PYHTON ENVIRNMENT

conda create -n bohack python

Then conda activate bohack

pip install -r requirements.txt

USE THE INITIALIZATION SCRIPT

init_data.py

example of use:

DATASET = Evaluation_data()

(
    X_init,
    y_init,
    X_candidate,
    y_candidate, 
    LIGANDS_INIT,
    LIGANDS_HOLDOUT,
    exp_init,
    exp_holdout,
) = DATASET.get_init_holdout_data()

We initialize with the complete set of experiments (all solvents and temperatures) for a single ligand. X_init and y_init are the representation vector of a reaction using ECFP4 and yields respectively used to initialize the BO.

X_candidate and y_candidate are the holdout set corresponding to experiments not yet performed. LIGANDS_INIT is the list of ligands included, LIGANDS_HOLDOUT are the ligands not yet included.

exp_init is the list of initial experimental conditions - exp_holdout again corresponds to the holdout set.

EXPERIMENTATION AND MODEL FITTING TIME

Model fitting: takes 0.5 h regardless of batch size

Experimentation time: a single experiment (batch size one) takes 1 h. Preparing the first experiment takes most of the time. Say three additional experiments are added. It will take less time, say only tadd = 0.5 h each. Thus, the total time for the batch is t = 1 h (base time) + 3*0.5 h = 2.5 h. We will consider tadd to be known and use it to study its relationship with batch size.