Detail

Semiconductor defect impurity levels

rjacobs3_wisc/Semiconductor_defect_levels_predictor
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Maciej Polak; Ryan Jacobs; Arun Mannodi-Kanakkithodi; Maria Chan; Dane Morgan
Scikit-learn estimator
GradientBoostingRegressor
materials science

Input

List of 15 elemental and one-hot encoded features to evaluate model. The list includes: M_3site, M_i_3site, M_i_neut_site, M_i_5site, M_5site, charge_from, charge_to, epsilon, CovalentRadius_max_value, ElectronAffinity_composition_average, NUnfilled_difference, phi_arithmetic_average, Site1_AtomicRadii_arithmetic_average, Site1_BCCvolume_padiff_differenc, Site1_HHIr_composition_average
Type: ndarray
Shape: ['None', '15']

Output

Predictions of semiconductor defect level energies (in eV)
Type: ndarray
Shape: ['None']

Run with DLHub SDK

from dlhub_sdk.client import DLHubClient
X = get_my_data() #replace this
dl = DLHubClient()
dl.run('rjacobs3_wisc/Semiconductor_defect_levels_predictor', X)

Get More Info with DLHub SDK

from dlhub_sdk.client import DLHubClient
dl = DLHubClient()
dl.describe_servable('rjacobs3_wisc/Semiconductor_defect_levels_predictor')

DLHub SDK Installation

pip install dlhub_sdk

DLHub SDK documentation