Research Of Prediction Model For Power Conversion Efficiency Based On Ensemble Learning

Power Conversion Efficiency(PCE)is the most important parameter for the Dye Sensitized Solar Cell(DSSC)performance evaluation,So the prediction accuracy of PCE directly affects the performance of DSSC.However,due to the complex structure of the solar cell device,it is difficult to use the quantum chemistry calculation method to directly calculate the value of PCE from the molecular structure properties.The machine learning methods can effectively bypass the complicated experimental process and directly construct the quantitative relationship between the structure of the fuel molecule and PCE.In this research,combined with quantum mechanics(QM)and machine learning(ML)to establish a quantum chemistry and machine learning correction model,referred to as QM/ML model,which can combine with their advantages to predict PCE of DSSC.Firstly,the properties of fuel molecules are calculated based on the STO-3G and 6-31G*basis sets using the density functional method B3LYP.Then,three machine learning methods are used:Support Vector Machine(SVM),General Regression Neural Network(GRNN),Classification and Regression Tree(CART)to construct a ensemble learner SVM-GRNN-CART(SGC).Finally,SGC combines three kinds of feature selection methods Plus-L Minus-R Selection algorithm,Randomized Lasso algorithm,Genetic algorithm to construct ensemble learner cascaded regression model.The output of the first stage of the cascaded regression model is Short-Circuit Photocurrent Density(Jsc),Open Circuit Voltage(Voc),and Fill Factor(FF),which are used as input to the second level regression model to predict the power conversion efficiency.Experimental results show that compared with the single learner SVM,GRNN,CART and homogeneity ensemble learner Random Forest(RF),ensemble learner(SGC)cascaded regression model have obvious advantages over single learner regression model and homogeneity ensemble learner RF in predicting ability,goodness of fit,and stability.Especially on the STO-3G basis sets,SGC combined with+L-R to predict the PCE get the best prediction results,in which the mean absolute error(MAE)is 0.37(%),the root of mean square error(RMSE)is 0.50(%)and the coefficient of determination(R~2)is 0.89.This study shows that the ensemble learner cascade model(SGC)can more effectively predict the PCE of DSSC,especially on the time-consuming low STO-3G basis set,we get better prediction results which provide an effective tool for predicting and designing new dye molecules and save the cost for experimental synthesis.