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.

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.

The preparation parameters of PM6:Y6 non-fullerene organic solar cells (OSCs) have significant influence on the power conversion efficiency (PCE). Herein, machine learning (ML) models are applied for analyzing the quantitative effects of the PCE on PM6:Y6 non-fullerene OSCs from the perspective of fabrication parameters. Random Forest (RF) model has the best evaluation performance and is considered as the best model among the six different algorithms. The Pearson correlation coefficient, coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error of the test set in the RF model are 0.836, 0.668, 0.695, 0.538, and 0.035, respectively. In addition, the most important preparation parameters for PCE are the effective area of OSCs, type of solvent and solvent additives, spin-coating speed, total concentration, annealing temperature, annealing time from SHAP analysis. The optimized PCE can be obtained in PM6:Y6 non-fullerene OSCs when the effective area is between 4 and 6 mm2, the spin-coating speed is 3000 rpm, and the total concentration is 18 mg ml−1, respectively. The contour map further reflects that the optimal annealing temperature are 80 or 105 °C, and the corresponding annealing time are 5 or 9 min, respectively. Based on the above preparation conditions, we verified through the experimental design that the optimal PCE is 16.91%, and the relative error with the ML prediction is only 1.24%. Our research provides low time and cost guidance on preparation parameter matching for the development of high-performance the PM6:Y6 non-fullerene OSCs.

Flexible and binderless electrodes have become a promising candidate for the next generation of flexible power storage devices. However, developing high-performance electrode materials with high energy density and a long cycle life remains a serious challenge for sodium-ion batteries (SIBs). The main issue is the large volume change in electrode materials during the cycling processes, leading to rapid capacity decay for SIBs. In this study, flexible electrodes for a SnSb alloy–carbon nanofiber (SnSb@NC) membrane were successfully synthesized with the aid of hydrothermal, electrospinning and annealing processes. The as-prepared binderless SnSb@NC flexible anodes were investigated for the storage properties of SIBs at 500 °C, 600 °C and 700 °C (SnSb@NC-500, SnSb@NC-600 and SnSb@NC-700), respectively. And the flexible SnSb@NC-700 electrode displayed the preferable SIB performances, achieving 240 mAh/g after 100 cycles at 0.1 A g⁻¹. In degree-dependent I-V curve measurements, the SnSb@NC-700 membrane exhibited almost the same current at different bending degrees of 0°, 45°, 90°, 120° and 175°, indicating the outstanding mechanical properties of the flexible binderless electrodes.