Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets
Quantifying charge-state transition energy levels of impurities in
semiconductors is critical to understanding and engineering their
optoelectronic properties for applications ranging from solar photovoltaics to
infrared lasers. While these transition levels can be measured and calculated
accurately, such efforts are time-consuming and more rapid prediction methods
would be beneficial. Here, we significantly reduce the time typically required
to predict impurity transition levels using multi-fidelity datasets and a
machine learning approach employing features based on elemental properties and
impurity positions. We use transition levels obtained from low-fidelity (i.e.,
local-density approximation or generalized gradient approximation) density
functional theory (DFT) calculations, corrected using a recently proposed
modified band alignment scheme, which well-approximates transition levels from
high-fidelity DFT (i.e., hybrid HSE06). The model fit to the large
multi-fidelity database shows improved accuracy compared to the models trained
on the more limited high-fidelity values. Crucially, in our approach, when
using the multi-fidelity data, high-fidelity values are not required for model
training, significantly reducing the computational cost required for training
the model. Our machine learning model of transition levels has a root mean
squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid
functional values when averaged over 14 semiconductor systems from the II-VI
and III-V families. As a guide for use on other systems, we assessed the model
on simulated data to show the expected accuracy level as a function of bandgap
for new materials of interest. Finally, we use the model to predict a complete
space of impurity charge-state transition levels in all zinc blende III-V and
II-VI systems.