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