Computational Designing of Triphenylamine Dyes with Broad and Red‐shifted Absorption Spectra for Dye‐sensitized Solar Cells using Multi‐Thiophene Rings in π‐Spacer

Mahmood, Asif; HussainTahir, Mudassir; Irfan, Ahmad; Khalid, Bilal

doi:10.1002/bkcs.10526

Cited by 82 publications

(24 citation statements)

References 27 publications

(39 reference statements)

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“…These calculations required a long time. [20][21][22] Calculated energy levels are used to predict power conversion efficiency (PCE) through Scharber's model. However, this model is not accurate.…”

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…[ 19–22 ] Even high‐speed computational facilities are available; longer time is also an issue. [ 23–26 ] Machine learning (ML) is a more suitable solution for these issues; it is fast and marginal computational cost is required. [ 27,28 ] However, sufficient amount of data are necessary because its prediction ability depends on quality and volume of data.…”

Section: Introductionmentioning

confidence: 99%

“…[19][20][21][22] Even high-speed computational facilities are available; longer time is also an issue. [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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“…With the development of computer technology, some properties of the chemical can be predicted by the developed programs, for example, ultraviolet‐visible and fluorescence spectra can be simulated by Materials Studio software and Gaussian software according to the luminescence mechanism. In this study, the maximum absorption and maximum emission wavelengths of the synthesised dye were calculated by Gaussian 09 based on time‐dependent density functional theory (TD‐DFT).…”

Section: Introductionmentioning

confidence: 99%

Simulation, synthesis, characterisation and dyeing properties of a fluorescent hemicyanine dye

Tang

Qin

et al. 2019

Coloration Technology

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In this study, a hemicyanine dye, trans-4-(4-(diethylamino)styryl)-1-ethylpyridinium bromide (DYE-BD), was synthesised and simulated. The maximum absorption wavelength of DYE-BD was calculated with the same basis set by using the Gaussian 09 software based on different methods, while time-dependent density functional theory was applied to simulate the maximum emission wavelength of the designed dye. The changes in enthalpy and Gibbs free energy were simulated at the b3lyp/6-31G(d) level, and energy changes in the luminescence process were assessed using mathematical methods. Furthermore, DYE-BD was used to colour acrylic fabric following the typical dyeing procedure for cationic dyes. The photostability of the synthesised dye was analysed using quantum theory and mathematical methods. The synthesised dye, analysed using the Ecological Structure Activity Relationships class program, had no toxicity to aquatic life, while the acrylic fabrics dyed with DYE-BD met the fluorescent orange requirements for background and combined performance materials according to the EN ISO 20471:2013 standard. 24 | TANG eT Al.

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Computational Designing of Triphenylamine Dyes with Broad and Red‐shifted Absorption Spectra for Dye‐sensitized Solar Cells using Multi‐Thiophene Rings in π‐Spacer

Cited by 82 publications

References 27 publications

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

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

Simulation, synthesis, characterisation and dyeing properties of a fluorescent hemicyanine dye

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