Informatics-aided bandgap engineering for solar materials

Abstract

This paper predicts the bandgaps of over 200 new chalcopyrite compounds for previously untested chemistries. An ensemble data mining approach involving Ordinary Least Squares (OLS), Sparse Partial Least Squares (SPLS) and Elastic Net/Least Absolute Shrinkage and Selection Operator (Lasso) regression methods coupled to Rough Set (RS) and Principal Component Analysis (PCA) methods was used to develop robust quantitative structure – activity relationship (QSAR) type models for bandgap prediction. The output of the regression analyses is the predicted bandgap for new compounds based on a model using the descriptors most related to bandgap. Feature ranking algorithms were then employed to: (i) assess the connection between bandgap and the chemical descriptors used in the predictive models; and (ii) understand the cause of outliers in the predictions. This paper provides a descriptor guided selection strategy for identifying new potential chalcopyrite chemistries materials for solar cell applications.