Machine learning assisted designing of non-fullerene electron acceptors: A quest for lower exciton binding energy.

The designing of acceptors materials for the organic solar cells is a hot topic. The normal experimental methods are tedious and expensive for large screening. Machine learning guided exploration is more suitable solution. Bagging regression, random forest regression, gradient boosting regression, and linear regression are trained to predict exciton binding energy. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) methodology has utilized for designing of new non-fullerene acceptors (NFAs). The predicted values were used to select the designed NFAs. On the selected NFAs, clustering and chemical similarity analyses are also performed. Chemical fingerprints are used for this purpose, and the synthetic accessibility score of the new NFAs is also investigated.30 NFAs have selected with low exciton binding energy values. This approach will allow for the rapid screening of NFAs for organic solar cells. Our proposed framework stands out as a valuable tool for strategically selecting the most effective NFAs for organic solar cells and offers a streamlined approach for material discovery.