of Ravishankar University–B, 35 (1), 8-14 (2022)
Analysis of High Efficient
Perovskite Solar Cells Using Machine Learning
Shuklaa*, K. Anil Kumara, Madhu Allallaa,
Research Laboratory, S.O.S. in Electronics and Photonics, Pt. Ravishankar
Shukla University Raipur, Chhattisgarh- 492010
[Received: 10 December 2021; Revised: 10 February 2022; Accepted: 15
manufacturing along with high efficiency perovskite solar cell in photovoltaic
technology has everyone's attention. Perovskite, which is in the lead role in
solar cells, is full of characteristics such as high absorption coefficient,
low exciton binding energy, charge carrier capable of having better mobility as
well as more diffusion length and availability in suitable energy band. The
application of machine learning technology is proving to be a boon to ensure
optimum implementation with different properties in photovoltaic device,
design, simple construction process and low-cost price. Machine learning is a
branch of artificial intelligence which includes large data aggregation,
precise structure property installation, demonstration and final model after
model validation. The most of the source of database is the simulation and
experimental results, calculations and related literature surveys which have a
comprehensive compilation of the performance of hybrid perovskite device,
collection of structures and properties of elements. Structure-property
relationship installation comes under feature engineering which establishes a
clear relationship between structure and the properties. In other demonstration
process, proper algorithms are selected, data is generated and tested as well
as pure estimated values are taken. This article contains a detailed discussion
on the involvement of machine learning technology to build high-performance
Perovskite solar cells. Proper selection as well as designing of active
perovskite absorbent layer by machine learning successfully establishes results
by including other parts such as non-toxic (lead free) and stability. Mature
machine learning technology becomes a very essential method in determining the
solvent combination of hybrid perovskite and in estimating design of the entire
solar cell to ensure optimum implementation in the sector of perovskite solar
technology. Finally, a phased concept has been briefly discussed to meet the
challenges of machine learning and potential future compatibilities related to
Keywords: Perovskite solar cells, machine learning,
artificial intelligence, algorithms.
solar cell is considered as a revolution in photovoltaic solar cell technology.
Its features such as low cost, easy fabrication, low temperature chemical
synthesis and manufacturing process which is not commonly found in popular
silicon-based solar cells (Green et al., 2019; Saliba
et al., 2019). Currently, Perovskite
solar cell reports a high-power conversion efficiency (PCE) of about 25.2 %
(Sahli et al., 2018) Perovskite solar cells have high efficiency due to
effective optical absorption coefficient, greater carrier diffusion distance
capacity, low defects and increased properties like negligible recombination
(recombination). However, continuous corrosion of perovskite stability in the
presence of water and oxygen is a matter of research (Koocher et al.,
2015; Müller et al., 2015). With this
problem focused, many research works are being carried out everywhere in the
country and abroad to increase the stability of perovskite compound. The main
objectives of machine learning are selection of new perovskite substances under
various interfacial technologies.
Figure 1. Features of machine learning
machine learning technology becomes a very essential method in determining the
liquid combination of hybrid Perovskite and the estimate design of the entire
solar cell to ensure optimum implementation in the Perovskite based solar
technology sector. This article discusses a phase concept to meet the
challenges of machine learning and potential future compatibilities related to
of the machine learning’s applications in the research field of perovskite
properties have been established to study and analyze the perovskite absorbent
layer structure and various characteristics of solar cells. It is through these
qualities that we also explain the characteristics of the ideal perovskite. The
common molecular formula of perovskite compound is ABX3 (example:
calcium titanium oxide CaTiO3 etc.). This photovoltaic perovskite
contains organic positive ions (e.g. methyl ammonium CH3NH3+etc.)
and inorganic cation (e.g. lead Pb+2, tin Sn+2 etc.)
respectively in A and B.X displays a halide resin e.g. I-, Cl-,
Br-etc. As a result,
perovskite compounds are formed MAPbI3, MAPbBr3 and MAPbI3-xClx,
etc. The formability and durability of perovskite is explained by goldsmith
tolerance coefficient Tf,
octahedral coefficient μ and other new versions (Goldschmidt et al., 1926;
Allam et al., 2018; Bartel et al., 2019). The probability of building a
permanent halide perovskite structure increases when the experimental value of
is between 0.81 and 1.11 and the quantity of octahedral coefficient μ is
between 0.44 and 0.90 and the position of the Tf
being 1 is the ideal cubic-3D perovskite (Kim et al., 2014; Johnsson et al.,
2007).] ∆H, ∆L, HOMO, LUMO and Eg
determine the main characteristics i.e. Jsc,
FF and PCE (Li et al., 2019). The results of the formation energy, electronic
dynamics and light absorption are determined by the halide composition,
electronic structures, unified figure of merit respectively (Yang et al., 2020;
Wang et al., 2020). Machine learning has been used for experimental and other
device characteristic analysis based on research articles published over the
last decade. These works provide a model for development of new permanent halide
perovskite as well as suitable ETL, HTL, selection of solvents and process for
fixed thickness for highly performance device (Odabas et al., 2019; Odabas et
al., 2020; Ren et al., 2019). The machine learning method ensures search and
optimum performance of appropriate conditions for achieving Reap-Rest-Recovery
Cycle (3-R Cycle) i.e. dynamic performance (Howard et al., 2019). Perovskite
solar cell has shown very low recombination with high PCE when it has good
quality and low defect density. Therefore, good crystallization of perovskite
is one of the essential needs. The perovskite crystallization process has been
guided by analysis of crystal pictures and basic factors in harmony with
convolution neural network with machine learning (Kirman et al., 2020). Machine
learning techniques have been developed to study the imitation of phase
transition hybrid perovskite substances (Jinnouchi et al., 2019).Figure 2. Methods used in machine learning
learning can understand by dividing it into the main five parts. In the above
figure 2, the main five parts of machine learning are description, clustering,
classification, estimation and association (Irwin et al., 2005). Simple
statistical analysis generally falls under description. The large data
describes graphically in it (Tukey et al., 1977). The process of doing large
data in small homogenous datasets based on their same properties is done by
clustering. There are two types of
clustering that are prevalent - the first type is k-means clustering technology
which is often used everywhere. There are other types hierarchical clustering
commonly used in the field of material research. Decision trees (DT), logistic regression
(LOR), k-nearest neighbor (KNN) algorithm, support vector machines (SVM),
Bayesian classification (BC), Artificial neural network (ANN) have been used as
common classification techniques. Classification divides data into classes.
These classes have been categorized by the magnitude and state (cold, dry, hot)
of the temperature of the substances. The forecast method establishes the
forecast by establishing a relationship between the input and output variables,
as indicated by name. For this, a number of technologies such as artificial neural networks
(ANN), multiple regression (MR), regression tree (RT), gradient boosting
regression (GBR), random forest (RF) and support vector regression (SVR) have
been used. Artificial neural networks based on deep learning algorithms the use
of substances has proved to be extremely useful in research. Association rule mining (ARM) ensures the
relationship between properties with large databases and identifying variables
(variable) through it. In order to achieve the same objective, the prevailing
aprior algorithm is used in data analysis (Larose et al., 2014). Other Subgroup
discovery methods are also used in material research. This technique creates
interesting associations between different variables and their special
properties as desired (Helal et al., 2016; Goldsmith, et al., 2017). Figure 3
shows the phased process of modeling by machine learning technique. The purpose
of machine learning is to move from large database to the last task of the
model (Lei Zhang, et al., 2020). Perovskite Solar Cell (n-i-p type) model is
displayed in Figure 4. It proposes selection of latest Perovskite active layer
by model, selection of electron and whole transport layer in line with energy
level (HOMO and LUMO) of absorbent perovskite layer and selection of proper
electrode for charge termination. Through feature engineering, high absorption
of light as well as sustainability can be ensured for lead free halide by
altering the properties such as tolerance factor, octahedral factor, bandgap
energy and other ionic charges. Formability values are indicated the stability
of perovskite structure, determined by the calculations. It acts as the output
variable after data processing of machine learning. Machine learning plays the
key role to investigate the internal dissociation of energy and the structural
factors. Association rule mining/ decision trees are helpful to increase in
stability and diminish the degradation, employed to the preparation of dataset
and analysis of hysteresis data (Choudhary et al., 2019; Odabas et al., 2020).
Figure 3. Machine
learning steps to getting a model of perovskite solar cell Figure 4. Basic
compound-based research has recorded a historic growth over the years. In the
same vein, the use of machine learning in physics research has also increased
rapidly. The application of machine learning has given amazing momentum to the
journey of bringing perovskite solar cell technology from new born to adult. As
a result, a good number of research articles, review articles, magazine
reports, and comments have been published. The computing techniques and
experimental works for perovskite technology development are set to grow in
future. Similarly, development in
machine learning and data management technology is also possible. However,
going from research to experimental level by machine learning model or from
matter to device is a challenging task itself. It is enough to understand the
fact that the stability of matter does not guarantee the stability of the
device. The difference can be seen only because of the process of manufacturing
the device, storage and operational conditions, and dependence on maintenance
of different laboratories. The source of data required for this error
prevention is linked to a number of various sources, with a good quantity of
non-uniformity levels, mismatches and missing data, etc., which will yield good
and accurate results.
thankful for research facilities at Photonics Research Laboratory, School of
Studies in Electronics and Photonics, Pt. Ravishankar Shukla University,
Raipur, Chhattisgarh, India.
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