Basic example for using BayBE

This example shows how to create a campaign and how to use it. It is intended to be used as a first point of interaction with campaign after having read the corresponding user guide.

Necessary imports for this example

from baybe import Campaign
from baybe.objectives import SingleTargetObjective
from baybe.parameters import NumericalDiscreteParameter, SubstanceParameter
from baybe.searchspace import SearchSpace
from baybe.targets import NumericalTarget
from baybe.utils.dataframe import add_fake_measurements

Setup

This example presents the optimization of a direct Arylation reaction in a discrete space. For this, we require data for solvents, ligands and bases.

dict_solvent = {
    "DMAc": r"CC(N(C)C)=O",
    "Butyornitrile": r"CCCC#N",
    "Butyl Ester": r"CCCCOC(C)=O",
    "p-Xylene": r"CC1=CC=C(C)C=C1",
}

dict_base = {
    "Potassium acetate": r"O=C([O-])C.[K+]",
    "Potassium pivalate": r"O=C([O-])C(C)(C)C.[K+]",
    "Cesium acetate": r"O=C([O-])C.[Cs+]",
    "Cesium pivalate": r"O=C([O-])C(C)(C)C.[Cs+]",
}

dict_ligand = {
    "BrettPhos": r"CC(C)C1=CC(C(C)C)=C(C(C(C)C)=C1)C2=C(P(C3CCCCC3)C4CCCCC4)C(OC)="
    "CC=C2OC",
    "Di-tert-butylphenylphosphine": r"CC(C)(C)P(C1=CC=CC=C1)C(C)(C)C",
    "(t-Bu)PhCPhos": r"CN(C)C1=CC=CC(N(C)C)=C1C2=CC=CC=C2P(C(C)(C)C)C3=CC=CC=C3",
}

We define the chemical substances parameters using the dictionaries defined previously. Here, we use "MORDRED" encoding, but others are available. We proceed to define numerical discrete parameters temperature and concentration and create the search space.

solvent = SubstanceParameter("Solvent", data=dict_solvent, encoding="MORDRED")
base = SubstanceParameter("Base", data=dict_base, encoding="MORDRED")
ligand = SubstanceParameter("Ligand", data=dict_ligand, encoding="MORDRED")

temperature = NumericalDiscreteParameter(
    "Temperature", values=[90, 105, 120], tolerance=2
)
concentration = NumericalDiscreteParameter(
    "Concentration", values=[0.057, 0.1, 0.153], tolerance=0.005
)

parameters = [solvent, base, ligand, temperature, concentration]

searchspace = SearchSpace.from_product(parameters=parameters)

In this example, we maximize the yield of a reaction and define a corresponding objective.

objective = SingleTargetObjective(target=NumericalTarget(name="yield", mode="MAX"))

We now finally create the campaign using the objects configure previously.

campaign = Campaign(
    searchspace=searchspace,
    objective=objective,
)

Getting a recommendation and adding measurements

We use the recommend() function of the campaign for getting measurements.

recommendation = campaign.recommend(batch_size=2)

print("\n\nRecommended measurements with batch_size = 2: ")
print(recommendation)

Recommended measurements with batch_size = 2: 
           Solvent               Base         Ligand  Temperature  \
120  Butyornitrile     Cesium acetate      BrettPhos        105.0   
384       p-Xylene  Potassium acetate  (t-Bu)PhCPhos        120.0   

     Concentration  
120          0.057  
384          0.057  

Adding target values is done by creating a new column in the recommendation dataframe named after the target. In this example, we use the add_fake_measurements() utility to create fake results. We then update the campaign by adding the measurements.

add_fake_measurements(recommendation, campaign.targets)
print("\n\nRecommended experiments with fake measured values: ")
print(recommendation)

Recommended experiments with fake measured values: 
           Solvent               Base         Ligand  Temperature  \
120  Butyornitrile     Cesium acetate      BrettPhos        105.0   
384       p-Xylene  Potassium acetate  (t-Bu)PhCPhos        120.0   

     Concentration      yield  
120          0.057  25.663051  
384          0.057  29.096702  

campaign.add_measurements(recommendation)