Optimization of Solar Cell Production Lines Using Neural Networks and Genetic Algorithms

Buratti, Yoann; Eijkens, Casper; Hameiri, Ziv

doi:10.1021/acsaem.0c01207

Cited by 12 publications

(7 citation statements)

References 19 publications

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“…Further down the value chain, ML and DL are also finding innovative applications. An example of this is [93], where neural networks and genetic algorithms were employed for the optimization of solar cell production lines, showing a potential increase in cell efficiency from 18.07% to 19.45%. Here, the authors noted that ML could outperform the traditional design of experiment (DoE) in optimizing the solar cell production line.…”

Section: The Role Of Ai and How It Can Help To Solve Some Of The Pv C...mentioning

confidence: 99%

Silicon Solar Cells: Trends, Manufacturing Challenges, and AI Perspectives

Di Sabatino,

Hendawi,

Garcia

2024

Crystals

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Photovoltaic (PV) installations have experienced significant growth in the past 20 years. During this period, the solar industry has witnessed technological advances, cost reductions, and increased awareness of renewable energy’s benefits. As more than 90% of the commercial solar cells in the market are made from silicon, in this work we will focus on silicon-based solar cells. As PV research is a very dynamic field, we believe that there is a need to present an overview of the status of silicon solar cell manufacturing (from feedstock production to ingot processing to solar cell fabrication), including recycling and the use of artificial intelligence. Therefore, this work introduces the silicon solar cell value chain with cost and sustainability aspects. It provides an overview of the main manufacturing techniques for silicon ingots, specifically Czochralski and directional solidification, with a focus on highlighting their key characteristics. We discuss the major challenges in silicon ingot production for solar applications, particularly optimizing production yield, reducing costs, and improving efficiency to meet the continued high demand for solar cells. We review solar cell technology developments in recent years and the new trends. We briefly discuss the recycling aspects, and finally, we present how digitalization and artificial intelligence can aid in solving some of the current PV industry challenges.

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Section: The Role Of Ai and How It Can Help To Solve Some Of The Pv C...mentioning

confidence: 99%

Silicon Solar Cells: Trends, Manufacturing Challenges, and AI Perspectives

Di Sabatino,

Hendawi,

Garcia

2024

Crystals

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show abstract

“…ML, as a technique for studying how to use computers to simulate human learning activities, aims to help researchers mine and understand the laws behind big data across a wide range of fields, such as the intersection of physics, chemistry, electronics, and materials science [ 21 , 22 , 23 ]. Nowadays, ML has been widely applied in the performance prediction of PSCs and the screening and design of new materials [ 24 , 25 , 26 , 27 , 28 ], and some work related to feature engineering and algorithm optimization to improve the prediction accuracy has been reported [ 29 , 30 ]. For example, Haibo Ma’s group used the gradient boosting regression tree (GBRT) model to conduct high-throughput screening on about 10,000 candidate materials, identifying important structural units and proposing 126 new material structures, with a predictive efficiency of more than 8% [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Providing a Photovoltaic Performance Enhancement Relationship from Binary to Ternary Polymer Solar Cells via Machine Learning

Cao,

2024

Polymers

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Ternary polymer solar cells (PSCs) are currently the simplest and most efficient way to further improve the device performance in PSCs. To find high-performance organic photovoltaic materials, the established connection between the material structure and device performance before fabrication is of great significance. Herein, firstly, a database of the photovoltaic performance in 874 experimental PSCs reported in the literature is established, and three different fingerprint expressions of a molecular structure are explored as input features; the results show that long fingerprints of 2D atom pairs can contain more effective information and improve the accuracy of the models. Through supervised learning, five machine learning (ML) models were trained to build a mapping of the photovoltaic performance improvement relationship from binary to ternary PSCs. The GBDT model had the best predictive ability and generalization. Eighteen key structural features from a non-fullerene acceptor and the third components that affect the device’s PCE were screened based on this model, including a nitrile group with lone-pair electron, a halogen atom, an oxygen atom, etc. Interestingly, the structural features for the enhanced device’s PCE were essentially increased by the Jsc or FF. More importantly, the reliability of the ML model was further verified by preparing the highly efficient PSCs. Taking the PM6:BTP-eC9:PY-IT ternary PSC as an example, the PCE prediction (18.03%) by the model was in good agreement with the experimental results (17.78%), the relative prediction error was 1.41%, and the relative error between all experimental results and predicted results was less than 5%. These results indicate that ML is a useful tool for exploring the photovoltaic performance improvement of PSCs and accelerating the design and application with highly efficient non-fullerene materials.

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“…Unfortunately, the materials science community is often dealing with a small dataset and suffers from the “black box” issue in the machine learning process. As a result, machine learning processes with improved interpretability − and scientific insights are highly desired.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Photoelectrochemical Photovoltage and Stability of Molecular Interlayer-Modified Halide Perovskite in Water: Insights from Interpretable Machine Learning and Symbolic Regression

Zhang

et al. 2023

ACS Appl. Energy Mater.

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Interpretable machine learning models are desired for materials and chemical design processes, while the stable optoelectronic properties of the halide perovskite materials in hostile conditions such as in water are prerequisites for their wider industrial deployment. In this study, we demonstrate an experimentally verified interpretable machine learning pipeline coupled with a symbolic regression method to optimize and understand the stability and photovoltage of the molecular interfacial layer-modified perovskite film in aqueous solution. An accurate machine learning model is achieved via the random forest algorithm; this leads to the successful experimental validation of a champion CH 3 NH 3 PbI 3 /bimolecule/TiO 2 system giving a stable photovoltage output in water with two interlayer molecules, basic violet 7 and rhodamine B. The experimental stability and photovoltage outputs of the machine learning-predicted sample in the aqueous solution are enhanced by 4 and 16.7% compared with those of the untreated CH 3 NH 3 PbI 3 film under the same aqueous conditions; the atomic adsorption structures are then investigated via density functional theory calculations. A t-SR symbolic regression method is developed to design relevant molecular descriptors, which employs a traversal algorithm comparing 8.95 million expressions and obtains a descriptor with 18% improvement. The present study provides a machine learning platform to accelerate the design of stable and high-performance perovskite films in extreme conditions, and the method can be elaborated to other surface systems.

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Optimization of Solar Cell Production Lines Using Neural Networks and Genetic Algorithms

Cited by 12 publications

References 19 publications

Silicon Solar Cells: Trends, Manufacturing Challenges, and AI Perspectives

Silicon Solar Cells: Trends, Manufacturing Challenges, and AI Perspectives

Providing a Photovoltaic Performance Enhancement Relationship from Binary to Ternary Polymer Solar Cells via Machine Learning

Optimizing Photoelectrochemical Photovoltage and Stability of Molecular Interlayer-Modified Halide Perovskite in Water: Insights from Interpretable Machine Learning and Symbolic Regression

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