The effects of the materials' energy levels on the V _oc in binary OSCs are analyzed and the energy level matching strategy of materials for high V _oc is delivered by machine learning. Experimental results verify the reliability of this machine learning approach.

Hematite is one of the most promising photoanode materials for the study of photoelectrochemical (PEC) water splitting because of its ideal bandgap with sufficient visible light absorption and stability in alkaline electrolytes. However, owing to the intrinsically high electron-hole recombination, the PEC performance of hematite is still far below that expected. The efficient charge separation can be achieved via growth of FeOOH on hematite photoanode. In this study, hematite nanostructures were successfully grown on the surface of iron foil by the simple immersion deposition method and thermal oxidation treatment. Furthermore, cocatalyst FeOOH was successfully added to the hematite nanostructure surface to improve charge separation and charge transfer, and thus promote the photoelectrochemical water splitting. By utilizing the FeOOH overlayer as a cocatalyst, the photocurrent density of hematite exhibited a substantial 86% increase under 1.5 V_RHE, while the onset potential showed an apparent shift towards the cathodic direction. This can be ascribed to the high reaction area for the nanostructured morphology and high electrocatalytic activity of FeOOH that enhanced the amount of photogenerated holes and accelerated the kinetics of water splitting.

Exciton dissociation and charge transport are important processes in the photoelectric conversion of organic solar cells (OSCs), directly affecting the performance of OSCs. In order to facilitate exciton dissociation, phase separation size should be as small as possible. However, for the sake of continuous charge transport, a large phase separation size is also necessary. To balance these two conflicting needs, it is of utmost importance to regulate the phase separation of the active layer. This Review mainly elaborated the smart tactics commonly adopted to phase separation optimization, aiming to understand how these treatments affect both the active layer phase separation and the performance of OSCs. Furthermore, we present an outlook on the new opportunities of phase separation regulation of the active layer. Finally, this Review may provide some reference for the researchers engaged in the research of OSCs.

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_oc) 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_oc 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_oc in ternary PSCs. Furthermore, the addition of the third component affects the energy offset between the donor and acceptor for increasing V_oc in ternary PSCs. More importantly, to obtain the maximum V_oc 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_oc 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_oc 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_oc in ternary PSCs for saving time and cost.