Prediction of PCE of fullerene (C60) derivatives as polymer solar cell acceptors by genetic algorithm–multiple linear regression

Abstract Quantitative structure property relationship study of Fullerene derivatives was studied to predict the power conversion efficiency of compounds as polymer solar cell acceptors. The data set was split into the training and test set by employing hierarchal cluster technique. The most relevant descriptors were selected using the genetic algorithm (GA) method. The predictive ability of the constructed model was evaluated using Y -randomization test, cross-validation and test set compounds. The GA–MLR model was built based on six molecular descriptors, and it revealed appropriate statistical results. The results suggested that some quantum-chemical descriptors play significant effects on increasing the PCE values.

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