Despite being the most commonly used hole transport layer for p-i-n perovskite solar cells, the conventional PEDOT:PSS layer is far from being optimal for the best photovoltaic performance. Herein, we demonstrate highly conductive thin DMSO-doped PEDOT:PSS layers which significantly enhance the light harvesting, charge extraction, and photocurrent production of organo-lead iodide devices. Both imaging and X-ray analysis reveal that the perovskite thin films grown on DMSO-doped PEDOT:PSS exhibit larger grains with increased crystallinity. Altogether, these improvements result in a 37% boost in the power conversion efficiency (PCE) compared to standard p-i-n photovoltaics with pristine PEDOT:PSS. Furthermore, we demonstrate that DMSO-doped PEDOT:PSS devices possess enhanced PCE durability over time which we attribute primarily to fill factor stability.

The p-i-n structure for perovskite solar cells has recently shown significant advantages in minimal hysteresis effects, and scalable manufacturing potential using low-temperature solution processing.

Traditionally, squaraine dyes have been studied and employed in biomedical research due to their excellent optical properties, and the molecules are being adopted in different research fields such as organic solar cells. In this study, we investigate correlations between solar cell performance and processing parameters of all‐small‐molecule bulk heterojunction solar cells comprising squaraine (SQ) as electron donor (D) and non‐fullerene small molecules (e.g., ITIC) as electron acceptor (A) with the help of machine learning (ML) and design of experiment (DoE) methods. Among the five predictive ML models tested with the selected parameters, the eXtreme gradient boosting model shows the satisfactory results with quite high coefficient of determination of 0.999 and 0.997 in training and testing sets, respectively. By measuring the contribution of each input variable to solar cell efficiency, four process parameters, that is, the total concentration, the ratio of D/A, the rotational speed of spin coating, and the annealing temperature, are found to be the key features strongly correlated to solar cell efficiency. From contour plots in DoE, the highest solar cell efficiency of approximately 5% can be predicted under the conditions of 15 mg mL⁻¹ in solution concentration, a 1:2 mix ratio of D and A, rotational speeds ranging from 800 to 900 rpm, and annealing temperatures within 100–110°C. Using the suggested parameter conditions, we fabricated solar cells, achieving a quite high efficiency of approximately 4%. Besides the global optimization conditions, we also employ the solvent vapor annealing combination to the thermal annealing to facilitate further mobilization of molecules and more optimized microstructure of bulk heterojunction films, resulting in a further enhancement in solar cell efficiency of more than 20%.

The frontier molecular orbitals of organic semiconductor materials play a crucial role in the performance of photoelectric devices, including organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), and organic photodetectors (OPDs). In this work, a model for predicting frontier molecular orbital of organic materials, including HOMO and LUMO levels, is established with the extreme gradient boosting algorithm and Klekota-Roth fingerprints. The correlation coefficients of HOMO or LUMO energy levels in the testing set are 0.75 and 0.84 in the transfer model from 11,626 DFT data in Harvard Energy database to 1198 experimental data in literature. The difference between the ML predicted value and the experimental value is smaller than the difference between ML prediction and DFT calculation, always less than 10%. Moreover, based on correlation and SHAP interpretability analysis, 13 key structural fragments influencing energy levels are selected to further verify the effective regulation of the frontier molecular orbital by the key structural fragments in practical applications. Considering the completely opposite regulatory functions of key structural fragments on HOMO and LUMO energy levels, four new Y6 derivatives, Y-PCP, Y-P6F, Y-PCF, and Y-P4FC, are designed to flexibly modify the HOMO and LUMO energy levels. The prediction trends of ML align closely with the computational trends from DFT. It is worth noting that the accuracy of LUMO energy level prediction by the prediction model makes up for the instability of DFT calculation on LUMO energy level. This work offers a cost-effective method to accelerate the acquisition of electronic properties of organic materials.

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 introduction of suitable passivation materials has led to a significant improvement in the power conversion efficiency (PCE) of perovskite solar cells (PSCs) in recent years. In this paper, the relationship between the molecular fingerprints of the passivation material and the PCE of p–i–n type PSCs is investigated using machine learning (ML). Data relating to around 100 passivation materials used to passivate the interfaces of perovskite/electron transport layers are collected. It is found that nitrogen atoms and acryloyl groups in the passivation material have the most influence on the PCE of p–i–n type PSCs. Therefore, a non-fullerene material, 3,9-bis(2-methylene-(3-(1,1-dicyanomethylene)-indanone)-5,5,11,11-tetralcis(4-hexylphenyl)-dithieno[2,3-d:2′,3′-d′]-s-indaceno[1,2-b:5,6-b′]-dithiophene (ITIC), which has both nitrogen atoms and acryloyl groups, is selected to passivate perovskite defects. Moreover, according to photoluminescence and time-resolved photoluminescence analyses, treatment with ITIC can enhance charge transport and diminish the defect density of the perovskite layer. Additionally, the Urbach energy of a perovskite film treated with ITIC is reduced from 127.1 meV to 96.8 meV, which reveals that the number of defects on the perovskite surface treated with ITIC is effectively reduced. More importantly, the introduction of ITIC dramatically improves the crystallinity and reduces the surface roughness of the perovskite films. Meanwhile, density-functional theory (DFT) calculations validate that incorporating ITIC into the anti-solvent effectively passivates the uncoordinated Pb²⁺ ions. In addition, compared with a non-treated PSC, the PCE of the ITIC-treated PSC shows a 20.97% enhancement. To sum up, ML has great application potential in the field of photovoltaics for choosing effective passivation materials in PSCs.