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

Janjua, Muhammad Ramzan Saeed Ashraf; Irfan, Ahmad; Hussien, Mohamed; Ali, Muhammad; Saqib, Muhammad; Sulaman, Muhammad

doi:10.1002/ente.202200019

Cited by 59 publications

(23 citation statements)

References 49 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

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“…In classification, the data set is divided into predefined groups. The range of a group controls the classification accuracy. − Classification only predicts the group in which the biological activity of a particular molecule will fall. To predict the biological activity value of a molecule, regression analysis is performed.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Assisted Prediction of the Biological Activity of Aromatase Inhibitors and Data Mining to Explore Similar Compounds

et al. 2022

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Designing molecules for drugs has been a hot topic for many decades. However, it is hard and expensive to find a new molecule. Thus, the cost of the final drug is also increased. Machine learning can provide the fastest way to predict the biological activity of druglike molecules. In the present work, machine learning models are trained for the prediction of the biological activity of aromatase inhibitors. Data was collected from the literature. Molecular descriptors are calculated to be used as independent features for model training. The results showed that the R 2 values for linear regression, random forest regression, gradient boosting regression, and bagging regression are 0.58, 0.84, 0.77, and 0.80, respectively. Using these models, it is possible to predict the activity of new molecules in a short period of time and at a reasonable cost. Furthermore, Tanimoto similarity is used for similarity analysis, as well as a chemical database is mined to search for similar molecules. Nonetheless, this study provides a framework for repurposing other effective drug molecules to prevent cancer.

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“…Different machine learning models show different prediction ability on same data [26][27][28]. Therefore, regression-based machine learning analysis is done using various models such as k-nearest neighbor (k-NN), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and linear regression (LR).…”

Section: Regression Machine Learningmentioning

confidence: 99%

Multidimensional modeling assisted mining of GDB17 chemical database: A search for polymer donors for organic solar cells and machine learning assisted performance prediction

Mebed

Jafri

Hakamy

et al. 2022

Int J of Quantum Chemistry

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Indeed, the designing of efficient material for organic solar cells is challenging and time‐consuming task. In this work, we have proposed a design pipeline to screen efficient polymer donors. Machine learning based models are trained to predict the power conversion efficiency (PCE). Structural descriptors are used as input to train machine learning models. The data of organic molecules is extracted from Photovoltaics database. Best buildings blocks are selected on the basis of similarity analysis using PBT7‐Th and MP6 as reference (standard). New polymers are designed using building blocks having high similarity. The PCE is predicted with the help of high‐performance machine learning model. Top three candidates are studied by molecular dynamics (MD) simulations. Packing behavior of polymer donors and their blend with fullerene acceptor is studied using radial distribution function (RDF). Density functional theory analysis is also performed on selected polymers. Electrostatic potential analysis has indicated the highly polar behavior that can result higher charge generation. Our proposed pipeline has ability to design and screen the polymer donors, as well as can predict their PCE without any experimentation. More importantly computational cost is marginal.

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

Cited by 59 publications

References 49 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

Machine Learning-Assisted Prediction of the Biological Activity of Aromatase Inhibitors and Data Mining to Explore Similar Compounds

Multidimensional modeling assisted mining of GDB17 chemical database: A search for polymer donors for organic solar cells and machine learning assisted performance prediction

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