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"))

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  Concentration
341       p-Xylene  Cesium acetate  BrettPhos        120.0          0.153
117  Butyornitrile  Cesium acetate  BrettPhos         90.0          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  Concentration  \
341       p-Xylene  Cesium acetate  BrettPhos        120.0          0.153   
117  Butyornitrile  Cesium acetate  BrettPhos         90.0          0.057   

         yield  
341  19.784182  
117  68.616625  

campaign.add_measurements(recommendation)