Machine learning assisted identification of the matched energy level of materials for high open circuit voltage in binary organic solar cells

Kuo, Way; Guo, Chaorong; Li, Zhennan; Zhang, Rui; Zhong-ke, Feng; Fang, Gengkun; Huang, Di; Liang, Jie; Zhao, Ling; Li, Zicha

doi:10.1039/d2me00265e

Cited by 12 publications

(5 citation statements)

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“…That is, the deeper-lying HOMO (D) and LUMO (A) are conducive to improving the PCE of the device. Kuo Wang et al’s study showed that the deeper-lying HOMO (D) was conducive to reducing the energy range to increase the V oc [ 28 ], while the deeper-lying LUMO (A) generally contributed to the construction of narrow band-gap acceptors to broaden the NIR absorption, and thus increase the J sc [ 40 ]. Usually, the effects of the LUMO (D) and HOMO (A) on the PCE seem to be ignored.…”

Section: Resultsmentioning

confidence: 99%

“…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%

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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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Section: Resultsmentioning

confidence: 99%

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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“…Previously, our team focused on voltage loss in non-fullerene OPVs, using machine learning to pinpoint key features for developing high-e ciency cells [44]. Additionally, we explore the energy level matching strategy of binary and ternary OPVs [45] and the effect of organic receptors on Voc [46]. Therefore, machine learning is pivotal in accelerate researching optoelectronic devices.…”

Section: Introductionmentioning

confidence: 99%

A universal platform of molecular orbital energy level prediction and molecular design for organic materials

Huang,

Peng,

Liang

et al. 2024

Preprint

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The design and optimization of organic materials with the specific functions for organic photovoltaic cells (OPV), organic light-emitting diodes (OLED), and organic photodetectors (OPD) with the customized performance are currently the time-consuming and costly process. Therefore, a molecular orbital energy level prediction platform for organic materials is established by utilizing the eXtreme Gradient Boosting (XGBT) algorithm and Klekota-Roth fingerprint (KRFP) in this study. And the prediction performance of prediction platform for predicting the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) of organic materials is characterized, which shows the accuracy is 99.0% and 97.5%, R is 0.88 and 0.93, RMSE is 0.077 and 0.126, MAE is 0.057 and 0.090, and MAPE is 0.01 and 0.025 in the training and test datasets, respectively. More importantly, thirteen key fragments are screened and their impact on HOMO and LUMO in organic materials is analyzed. Apparently, fluoromethane fragments can reduce HOMO and raise LUMO in organic materials, while Cycopropane fragments were observed to elevate HOMO and decrease LUMO. Based on the findings, Y6 molecules is modified to design four new Y6 derivatives, including Y6-DT, Y6-TF, Y6-TDF, and Y6-DFT for adjusting bandgap of organic materials. And the value difference of HOMO or LUMO in the new designed molecules between predicted by the platform and calculated by DFT is only below 5%. It is noteworthy that the platform prediction only costs an average time of 0.1 s. Moreover, this prediction platform also verifies the reported results in OLED and OPD-related literature, showing that the predicted accuracy is higher than 88.1%, the errors are limited to within 11.9%. All of these confirm the establishment of a cost-effective universal platform with high performance for accurately predicting and regulating the energy levels in organic materials.

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“…Machine learning (ML) allows researchers to excavate the hidden physical laws behind big data and draw reliable conclusions from the perspective of algorithms for more effectively accelerating material design and performance optimization in PSCs to save time and money [20][21][22]. Currently, ML has been widely applied in the field of PSCs [23][24][25][26][27]. Kakaraparthi Kranthiraja et al developed a series of novel π-conjugated polymer donor materials for NFAs based on the random forest (RF) model [23].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, our group applied ML to predict the champion PCE of ternary PSCs and screen the optimal doping ratio of the third component [26]. Furthermore, we also elucidated the matching energy levels of binary PSCs materials to enhance the V oc through ML [27]. Exploring organic materials and understanding hidden chemical information via ML have become a new research model.…”

Section: Introductionmentioning

confidence: 99%

Probing the Effect of Photovoltaic Material on Voc in Ternary Polymer Solar Cells with Non-Fullerene Acceptors by Machine Learning

Huang

Kuo

et al. 2023

Polymers

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The power conversion efficiency (PCE) of ternary polymer solar cells (PSCs) with non-fullerene has a phenomenal increase in recent years. However, improving the open circuit voltage (Voc) of ternary PSCs with non-fullerene still remains a challenge. Therefore, in this work, machine learning (ML) algorithms are employed, including eXtreme gradient boosting, K-nearest neighbor and random forest, to quantitatively analyze the impact mechanism of Voc in ternary PSCs with the double acceptors from the two aspects of photovoltaic materials. In one aspect of photovoltaic materials, the doping concentration has the greatest impact on Voc in ternary PSCs. Furthermore, the addition of the third component affects the energy offset between the donor and acceptor for increasing Voc in ternary PSCs. More importantly, to obtain the maximum Voc in ternary PSCs with the double acceptors, the HOMO and LUMO energy levels of the third component should be around (−5.7 ± 0.1) eV and (−3.6 ± 0.1) eV, respectively. In the other aspect of molecular descriptors and molecular fingerprints in the third component of ternary PSCs with the double acceptors, the hydrogen bond strength and aromatic ring structure of the third component have high impact on the Voc of ternary PSCs. In partial dependence plot, it is clear that when the number of methyl groups is four and the number of carbonyl groups is two in the third component of acceptor, the Voc of ternary PSCs with the double acceptors can be maximized. All of these findings provide valuable insights into the development of materials with high Voc in ternary PSCs for saving time and cost.

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Machine learning assisted identification of the matched energy level of materials for high open circuit voltage in binary organic solar cells

Abstract: With the application of the new material and device optimization method, binary bulk heterojunction organic solar cells (OSCs) have the outstanding performance in recent years. However, the open-circuit voltage (Voc)...

Cited by 12 publications

References 50 publications

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

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

A universal platform of molecular orbital energy level prediction and molecular design for organic materials

Probing the Effect of Photovoltaic Material on Voc in Ternary Polymer Solar Cells with Non-Fullerene Acceptors by Machine Learning

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