Probing the Effect of Photovoltaic Material on V in Ternary Polymer Solar Cells with Non-Fullerene Acceptors by Machine Learning.

The power conversion efficiency (PCE) of ternary polymer solar cells (PSCs) with non-fullerene has a phenomenal increase in recent years. However, improving the open circuit voltage (V) of ternary PSCs with non-fullerene still remains a challenge. Therefore, in this work, machine learning (ML) algorithms are employed, including eXtreme gradient boosting, K-nearest neighbor and random forest, to quantitatively analyze the impact mechanism of V in ternary PSCs with the double acceptors from the two aspects of photovoltaic materials. In one aspect of photovoltaic materials, the doping concentration has the greatest impact on V in ternary PSCs. Furthermore, the addition of the third component affects the energy offset between the donor and acceptor for increasing V in ternary PSCs. More importantly, to obtain the maximum V in ternary PSCs with the double acceptors, the HOMO and LUMO energy levels of the third component should be around (-5.7 ± 0.1) eV and (-3.6 ± 0.1) eV, respectively. In the other aspect of molecular descriptors and molecular fingerprints in the third component of ternary PSCs with the double acceptors, the hydrogen bond strength and aromatic ring structure of the third component have high impact on the V of ternary PSCs. In partial dependence plot, it is clear that when the number of methyl groups is four and the number of carbonyl groups is two in the third component of acceptor, the V of ternary PSCs with the double acceptors can be maximized. All of these findings provide valuable insights into the development of materials with high V in ternary PSCs for saving time and cost.