화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Journal of Industrial and Engineering Chemistry, Vol.21, 1058-1067, January, 2015
DOI10.1016/j.jiec.2014.05.016  Export Citation
Prediction of PCE of fullerene (C60) derivatives as polymer solar cell acceptors by genetic algorithm-multiple linear regression
Eslam Pourbasheer†, Alireza Banaei1, Reza Aalizadeh2, Mohammad Reza Ganjali3, Parviz Norouzi3, Javad Shadmanesh4, and Constantinos Methenitis4
  • Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran
  • 1Department of Chemistry, Payame Noor University, Tehran, Iran
  • 2Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
  • 3Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, PO Box 14155-6455, Tehran, Iran
  • 4Laboratory of Inorganic Chemistry and Technology, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, Athens 15771, Greece
E-mail:
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.
Keywords:Fullerene;Power conversion efficiency;Solar cells;QSPR;Genetic algorithm
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