Machine learning and molecular dynamics simulation-assisted evolutionary design and discovery pipeline to screen efficient small molecule acceptors for PTB7-Th-based organic solar cells with over 15% efficiency

Wang, Jinliang; Mahmood, Asif; Irfan, Ahmad

doi:10.1039/d1ta09762h

Cited by 141 publications

(74 citation statements)

References 65 publications

(63 reference statements)

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“…[55,56] Machine learning is gaining fame in organic solar cell research. [57,58] Machine learning is implemented through random forest (RF), linear regression (LR), k-nearest neighbor (k-NN) and support vector machine (SVM). Data is divided in training and test sets with various ratios, 70:30% training: test ratio has showed better performance for all models.…”

Section: Machine Learning Analysismentioning

confidence: 99%

Impact of halogens on electronic and photovoltaic properties of organic semiconductors: A multiscale computational modeling

Alwadai

Elqahtani

Khan

et al. 2022

J of Physical Organic Chem

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Indeed, structural and electronic behavior of organic semiconductors control their performance for organic solar cells. To attain the higher performance a deeper understanding of materials is required. Here, multi-scale computational modeling is used to study the effect of structural variation on the halogen substitution. Quantum chemical calculations, molecular dynamics simulations and machine learning are used. Halogens are introduced at the terminal position of electron-acceptors and their electronic properties are further examined. Quantum chemical analysis has shown that fluorinated and chlorinated acceptors have lower exciton binding energy, higher transfer integral, and lower reorganization energy; suggesting that these acceptors are better than others. Moreover, the power conversion efficiency of newly designed acceptor materials is also predicted through already trained machine learning models. Fluorinated and chlorinated acceptors showed higher PCE, but the difference is not very large as compared with other acceptors. Further, the mixing behavior of the designed acceptors with the polymer donor PBDB-T is investigated using the Florgy-Huggins parameter. The molecular packing of donor and acceptor molecules is studied using radial distribution function. Fluorinated and chlorinated acceptors showed lower Florgy-Huggins parameter and free energy of mixing. We believe that multiscale modeling has the potential to explore various electronic and photovoltaic aspects of organic semiconductors even before synthesis.

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Section: Machine Learning Analysismentioning

confidence: 99%

Impact of halogens on electronic and photovoltaic properties of organic semiconductors: A multiscale computational modeling

Alwadai

Elqahtani

Khan

et al. 2022

J of Physical Organic Chem

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“…In organic solar cells research community, machine learning is gaining fame. [26][27][28] However, it has not gained much success as it has gained in image recognition and translation. The major reason is the complex working principle of organic solar cells.…”

Section: Introductionmentioning

confidence: 99%

Machine‐Learning Analysis of Small‐Molecule Donors for Fullerene Based Organic Solar Cells

et al. 2022

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In recent years, development in organic solar cells speeds up and performance continuously increases. From the last few years, machine learning gains fame among scientists who are researching on organic solar cells. Herein, machine learning is used to screen the small‐molecule donors for organic solar cells. Molecular descriptors are used as input to train machine models. A variety of machine‐learning models are tested to find the suitable one. Random forest model shows best predictive capability (Pearson's coefficient = 0.93). New small‐molecule donors are also designed from easily synthesizable building units. Their power conversion efficiencies (PCEs) are predicted. Potential candidates with PCE > 11% are selected. The approach presented herein helps to select the efficient materials in short time with ease.

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“…It is not possible to study the large number of materials due to tedious and expensive experimental work. [ 18 ] The significance increase in the performance of OSCs is a result of longtime struggle. The discovery of new materials and optimization of processing conditions both have played equal role in improvement.…”

Section: Introductionmentioning

confidence: 99%

“…[23][24][25][26] Machine learning (ML) is a more suitable solution for these issues; it is fast and marginal computational cost is required. [27,28] However,…”

mentioning

confidence: 99%

Learning from Fullerenes and Predicting for Y6: Machine Learning and High‐Throughput Screening of Small Molecule Donors for Organic Solar Cells

et al. 2022

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In recent years, research on the development of organic solar cells has increased significantly. For the last few years, machine learning (ML) has been gaining the attention of the scientific community working on organic solar cells. Herein, ML is used to screen small molecule donors for organic solar cells. ML models are fed by molecular descriptors. Various ML models are employed. The predictive capability of a support vector machine is found to be higher (Pearson's coefficient = 0.75). The best small donors with fullerene acceptors are selected to pair with Y6. New small molecule donors are also designed taking into account quantum chemistry principles, using building units that are searched through similarity analysis. Their energy levels and power conversion efficiencies (PCEs) are predicted. Efficient small molecule donors with PCE > 13% are selected. This design and discovery pipeline provides an easy and fast way to select potential candidates for experimental work.

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Machine learning and molecular dynamics simulation-assisted evolutionary design and discovery pipeline to screen efficient small molecule acceptors for PTB7-Th-based organic solar cells with over 15% efficiency

Abstract: Organic solar cells are the most promising candidates for future commercialization. This goal can be quickly achieved by designing new materials and predicting their performance without experimentation to reduce the...

Cited by 141 publications

References 65 publications

Impact of halogens on electronic and photovoltaic properties of organic semiconductors: A multiscale computational modeling

Impact of halogens on electronic and photovoltaic properties of organic semiconductors: A multiscale computational modeling

Machine‐Learning Analysis of Small‐Molecule Donors for Fullerene Based Organic Solar Cells

Learning from Fullerenes and Predicting for Y6: Machine Learning and High‐Throughput Screening of Small Molecule Donors for Organic Solar Cells

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