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

Irfan, Ahmad; Hussien, Mohamed; Mehboob, Muhammad Yasir; Ahmad, Aziz; Janjua, Muhammad Ramzan Saeed Ashraf

doi:10.1002/ente.202101096

Cited by 30 publications

(7 citation statements)

References 60 publications

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“…The collected data is based on photovoltaic parameters of OSC devices that consist of SMDs and fullerene acceptors. The quality and quantity of data strongly effect the performance of machine learning model [26,27]. The volume of data is enough for machine learning analysis.…”

Section: Datasetmentioning

confidence: 99%

The use of machine learning, density functional theory, and molecular dynamics simulations for the designing and screening of efficient small molecule donors for organic solar cells

Katubi

Pembere

Mehboob

et al. 2022

Int J of Quantum Chemistry

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Indeed, a proper understanding of materials is necessary to get the full benefit from them. For this purpose, multiscale computational modeling is the ultimate need. For machine learning analysis, data is collected from the literature. Machine learning analysis is performed using molecular descriptors as independent parameters and power conversion efficiency (PCE) as dependent property. Various machine learning models are tried. The support vector machine (SVM) model has outperformed others. New donor materials that are small molecules are designed using both well‐known and new building blocks. Their PCE is predicted using a SVM model. The top 10 small molecule donors are further studied using density functional theory calculations. Their electronic behavior is studied. Reorganization energy, exciton binding energy and transfer integral are also calculated. Finally, the best three small molecule donors are selected for molecular dynamics simulations. Molecular packing and mixing of active layer materials is studied using radial distribution function. Our proposed framework has the ability to design potential donor materials in short time with marginal computational cost.

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Section: Datasetmentioning

confidence: 99%

The use of machine learning, density functional theory, and molecular dynamics simulations for the designing and screening of efficient small molecule donors for organic solar cells

Katubi

Pembere

Mehboob

et al. 2022

Int J of Quantum Chemistry

Self Cite

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“…In one of the latest papers published by Ahmad et al, [ 68 ] they discuss the implementation of ML to screen small‐molecule donors for OSCs and molecular descriptors feed ML methods. The coauthors collected a dataset of 340 OSC devices with donors represented as small molecules, while acceptors as fullerenes for the ML‐assisted pipeline suitable for small‐molecule donors for Y6 (an electron acceptor).…”

Section: Results and Analysismentioning

confidence: 99%

Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques

Bhatti,

Manzoor,

Michel

et al. 2023

Adv Energy and Sustain Res

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Machine learning (ML) and artificial intelligence (AI) methods are emerging as promising technologies for enhancing the performance of low‐cost photovoltaic (PV) cells in miniaturized electronic devices. Indeed, ML is set to significantly contribute to the development of more efficient and cost‐effective solar cells. This systematic review offers an extensive analysis of recent ML techniques in designing novel solar cell materials and structures, highlighting their potential to transform the low‐cost solar cell research and development landscape. The review encompasses a variety of ML approaches, such as Gaussian process regression (GPR), Bayesian optimization (BO), and deep neural networks (DNNs), which have proven effective in boosting the efficiency, stability, and affordability of solar cells. The findings of this review indicate that GPR combined with BO is the most promising method for developing low‐cost solar cells. These techniques can significantly speed up the discovery of new PV materials and structures while enhancing the efficiency and stability of low‐cost solar cells. The review concludes with insights on the challenges, prospects, and future directions of ML in low‐cost solar cell research and development.

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“…(5) Recently, machine learning has attracted more and more attention from researchers in the field of OSCs. 91,92 Since a large number of new FREAs are being reported now, the relationship between their structure and performance is becoming increasingly clear. Thus, based on the existing results of symmetric FREAs, asymmetric structure optimization can be accelerated through machine learning, and diverse asymmetric structures will be explored more efficiently.…”

Section: Discussionmentioning

confidence: 99%

Symmetry breaking: an efficient structure design of nonfullerene acceptors to reduce the energy loss in organic solar cells

Liu

Zheng

et al. 2023

J. Mater. Chem. C

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

Cited by 30 publications

References 60 publications

The use of machine learning, density functional theory, and molecular dynamics simulations for the designing and screening of efficient small molecule donors for organic solar cells

The use of machine learning, density functional theory, and molecular dynamics simulations for the designing and screening of efficient small molecule donors for organic solar cells

Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques

Symmetry breaking: an efficient structure design of nonfullerene acceptors to reduce the energy loss in organic solar cells

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