Revolutionizing polymer engineering for Photodetectors: A Machine Learning-Assisted paradigm for rapid materials discovery

Polymers are appealing candidates for photoelectric applications because of their intrinsic characteristics include flexibility in substrate compatibility, ease of manufacturing, room temperature operating conditions, and adaptable optoelectronic properties. In present study, we have used the machin...

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Bibliographic Details
Published in:Chemical physics 2024-06, Vol.582, p.112277, Article 112277
Main Authors: Zhou, Jing, Shoaib Ahmad Shah, Syed, Naeem, Sumaira, Siddique, Bilal, Khan, Numan, Ul Hassan, Abrar, El-Sheikh, Mohamed A., Elansary, Hosam O.
Format: Article
Language:English
Subjects:
Machine learning
Photodetectors
Polymers
Property predictions
Regressor models
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Summary:Polymers are appealing candidates for photoelectric applications because of their intrinsic characteristics include flexibility in substrate compatibility, ease of manufacturing, room temperature operating conditions, and adaptable optoelectronic properties. In present study, we have used the machine learning (ML) for property prediction and polymer designing. Multiple ML models are trained. 10,000 novel polymers are generated using automatic method. The synthetic accessibility of the chosen polymers is predicted. Structural similarity among the selected polymers is also calculated that indicated good structural similarity among the chosen polymers. By effectively identifying and optimizing novel polymers, the implemented methods substantially increase the chances of uncovering superior materials suited for advanced applications.
ISSN:0301-0104
DOI:10.1016/j.chemphys.2024.112277