Generation of Database of Polymer Acceptors and Machine Learning‐Assisted Screening of Efficient Candidates

ABSTRACT This paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion ef...

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Published in:International journal of quantum chemistry 2024-11, Vol.124 (21), p.n/a
Main Authors: Tahir, Mudassir Hussain, Khan, Naeem‐Ul‐Haq, Elhindi, Khalid M.
Format: Article
Language:English
Subjects:
Addition polymerization
Chemical synthesis
Cluster analysis
descriptors
Energy conversion efficiency
Machine learning
organic solar cells
Polymers
power conversion efficiency
Predictions
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Khan, Naeem‐Ul‐Haq
Elhindi, Khalid M.
description ABSTRACT This paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion efficiency (PCE). Histgradient boosting regressor has appeared as best model. Almost 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has been visualized and analyzed. Cluster analysis revealed significant differences among the selected polymers. Additionally, an assessment of synthetic accessibility for these polymers indicated that the majority can be synthesized with relative ease. Machine learning models are used to predict the power conversion efficiency (PCE). Nearly 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has visualized and analyzed.
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subjects Addition polymerization
Chemical synthesis
Cluster analysis
descriptors
Energy conversion efficiency
Machine learning
organic solar cells
Polymers
power conversion efficiency
Predictions
title Generation of Database of Polymer Acceptors and Machine Learning‐Assisted Screening of Efficient Candidates
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