Abstract
To unlock the full potential of PSCs, machine learning (ML) was implemented in this research to predict the best combination of mesoporous-titanium dioxide (mp-TiO2) and weight percentage (wt%) of phenyl-C61-butyric acid methyl ester (PCBM), along with the current density (Jsc ), open-circuit voltage (Voc ), fill factor (ff ) and energy conversion efficiency (ECE). Then, the combination that yielded the highest predicted ECE was selected as a reference to fabricate PCBM-PSCs with nanopatterned TiO2 layer. Subsequently, the PCBM-PSCs with nanopatterned TiO2 layers were fabricated and characterized to further understand the dual effects of nanopatterning depth and wt% of PCBM on PSCs. Experimentally, the highest ECE of 17.336% is achieved at 127 nm nanopatterning depth and 0.10 wt% of PCBM, where the Jsc ,Voc and ff are 22.877 mA/cm2, 0.963 V and 0.787, respectively. The measuredJsc , Voc , ff and ECE values show consistencies with the ML prediction. Hence, these findings not only revealed the potential of ML to be used as a preliminary investigation to navigate the research of PSCs, but also highlighted that nanopatterning depth has a significant impact onJsc , and the incorporation of PCBM on perovskite layer influenced the Voc and ff , which further boosted the performance of PSCs.
1. Introduction
Machine learning (ML), which belongs to an important branch of artificial intelligence, has gained fame in the fields of energy materials and solar cells.[1] ML is a science of getting computers to perform tasks without being explicitly programmed, and it can learn from past results and provide fast predictions of unknown results.[2] Furthermore, ML also can infer the potential rules and relationships among materials, and between materials and the features of the complex system composed of materials only through the data itself without knowing the physical laws.[3] With the proven capabilities of ML, ML can have a significant impact to greatly increase the speed of doing research in perovskite solar cells (PSCs). To optimize PSCs, the conventional method mostly depends on trial-and-error methods, which is not only time consuming, but has limited test options and leads to the cost of opportunity loss by doing something else with the available time. Therefore, by applying ML, researchers may examine a tremendous amount of data on PSCs and make predictions by uncovering patterns and relationships in the data. These predictions can then be used to guide experiments, enabling researchers to optimize the development of PSCs more efficiently than traditional methods.
Studies have shown that the performance of PSCs is hindered by low light harvesting. Specifically, the ability of PSCs to absorb light drops significantly between 650-800 nm and diminishes further when the wavelength exceeds 800 nm.[4-6] To overcome this issue, one of the strategies that has been implemented is light scattering. The basic idea of light scattering is to confine light propagation and extend the traveling distance of incident light within the photoelectrode film,[7] which can be explained using Rayleigh and Mie scattering theories. In general, Rayleigh scattering theory is applicable for small-sized particles, whose diameter is less than about one-tenth the wavelength of the incident light while Mie scattering theory is applicable to large particles, whose diameter is larger than the wavelength of the incident light.[8] Apart from that, the introduction of nano/microstructures at the glass/air interface have also been reported to increase the incident photon and the generation of photocurrent.[9, 10] Studies also reported that constructing nano/microstructures at the fluorine-doped tin oxide (FTO)/ titanium dioxide (TiO2) interface can increase the absorption length for the incident light due to the diffraction effect.[11-13] Therefore, to increase the light harvesting property in PSCs, Hwa-Young Yang et al. have also shown the relationship between the transmittance and the electron generation by varying the nanopatterning depth of the mesoporous titanium dioxide (mp-TiO2) layer.[14]
Next, to improve the electron transport in PSCs, a plethora of materials have been studied and carbon nanomaterials have been widely utilized in PSCs to enhance both the efficiency and stability due to their outstanding chemical, electrical, and mechanical characteristics. It has not only been used as an additive, but has also acted on potential structure alternatives for the charge carrier transportation layer, electrode, and active film.[15] Among carbon nanomaterials, fullerene nanometric-sized spherical shapes play an important role for efficient interfacing with perovskites, resulting in a homogeneous distribution of the carbon nanomaterials on the surface of light absorber, thus generating highly selective contacts for electron extraction.[16] Phenyl-C61-butyric acid methyl ester (PCBM), which is a fullerene derivative of the C60, is among the most employed carbon nanomaterials in PSCs due to the alignment of energy levels and high electron mobility.[17, 18] Previous research has demonstrated that by controlling the amount of PCBM in the lead(II) iodide (PbI2) precursor solution, the fill factor (ff ) increased significantly as PCBM are able to fill in the pinholes and vacancies between perovskite grains that produce continuous pathways for electron extraction. Moreover, when PCBM was mixed with perovskite to create a PCBM-perovskite structure, the charge recombination lifetime and carrier diffusion length increases as the electron was transported over longer distances in the film compared to pure perovskite film.[19]
Based on the mentioned articles, studies have shown that the most optimized nanopatterning depth will affect the current density (Jsc ), and the incorporation of PCBM into perovskite is able to significantly enhance the ff of the PSCs. Therefore, with the potential of ML to be used as a preliminary approach, it is expected that the combination of ML with light management and electron transport strategies will yield a better performance of PSCs. Herein, ML is used to assist the fabrication of PCBM-PSCs with nanopatterned TiO2 layer by deciphering the relationship between nanopatterning depth of mp-TiO2and weight percentage (wt%) of PCBM to predict the best combination of the two variables. Not only that, the photovoltaics parameters, that areJsc , open-circuit voltage (Voc ), ff and energy conversion efficiency (ECE) are also predicted by the ML. Subsequently, the PCBM-PSCs with nanopatterned TiO2 layer are prepared and characterized to further understand the dual effects of nanopatterning depth and wt% of PCBM on PSCs. Hence, these findings not only revealed the potential of ML to be used as a preliminary investigation to navigate the research of PSCs, but also highlighted that nanopatterning depths have significant impact on Jsc , and the incorporation of PCBM on perovskite layer influenced the Vocand ff , which further boosted the performance of PSCs.
2. Results and Discussion