Machine learning approach for predicting high JSC donor molecules in fullerene-typed organic solar cells

Abstract

This study aimed to identify donor molecules that enhance the JSC in fullerene-type organic thin-film solar cells using materials informatics. After performing principal component analysis and Random Forest for feature selection, LASSO and Ridge regressions and SVR were developed. A genetic algorithm generated 250 new donor molecules, and SVR predicted that (i) 4H-cyclopentadithiophene, (ii) fluorine-containing structures, and (iii) C = O groups adjacent to thiophenes improve the JSC.