Getting started

The MODNet package is built around two classes: MODData and MODNetModel.

Usage

The usual workflow is as follows:

from modnet.preprocessing import MODData
from modnet.models import MODNetModel

# Creating MODData
data = MODData(materials = structures,
               targets = targets,
              )
data.featurize()
data.feature_selection(n=200)

# Creating MODNetModel
model = MODNetModel(target_hierarchy,
                    weights,
                    num_neurons=[[256],[64],[64],[32]],
                    )
model.fit(data)

# Predicting on unlabeled data
data_to_predict = MODData(new_structures)
data_to_predict.featurize()
df_predictions = model.predict(data_to_predict) # returns dataframe containing the prediction on new_structures

Example Notebooks

Example notebooks and short tutorials can be found in the example_notebooks directory on the GitHub repo.

Pretrained Models

Two pretrained models are provided in pretrained/:

• Refractive index

• Vibrational thermodynamics

Download these models locally to path/to/pretrained/. Pretrained models can then be used as follows:

from modnet.models import MODNetModel

model = MODNetModel.load('path/to/pretrained/refractive_index')
# or MODNetModel.load('path/to/pretrained/vib_thermo')

Stored MODData

The following MODDatas are available for download:

• Formation energy on Materials Project (June 2018), on figshare

• Refractive index (upon request)

• Vibrational thermodynamics (upon request)

Download this directory locally to path/to/moddata/. These can then be used as follows:

from modnet.preprocessing import MODData

data_MP = MODData.load('path/to/moddata/MP_2018.6')

The MP MODData on figshare (MP_2018.6) is very useful for predicting a learned property on all structures from the Materials Project:

predictions_on_MP = model.predict(data_MP)