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      Molecular level understanding of the chalcogen atom effect on chalcogen-based polymers through electrostatic potential, non-covalent interactions, excited state behaviour, and radial distribution function

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          Abstract

          Multi-dimensional modelling was used to study the effect of chalcogen atoms on the non-covalent interactions, structural and electronic properties of polymer materials. Their bulk properties were also studied at the molecular level.

          Abstract

          Polymer donor materials have been considered as a game changer, especially in the early history of polymer solar cells. However, much progress is the result of hard work resulting from hit and miss experiments. A deeper understanding of the electronic behavior of polymeric materials is necessary to select efficient materials for polymer solar cells. A detailed computational analysis is performed on the chalcogen-based polymers CP1, CP2, and CP3 to study the effect of chalcogen atoms on their non-covalent interactions, structural and electronic properties. The alteration of the chalcogen atoms significantly changed the electronic and excited behavior of the polymers. Moreover, the chalcogen atoms also exerted a significant effect on nearby groups. Selenium had more of a polarization effect on molecules compared with other chalcogen atoms. Polymer:Y6 complexes were also studied to determine rules for donor:acceptor pair selection. Significance changes were observed on changing the chalcogen atoms. The sulfur and selenium-based polymers CP2 and CP3 exhibited higher density of states near to the Fermi level in comparison with the oxygen-based polymer CP1. The effect of chalcogen atoms on molecular packing and blend morphology was studied using molecular dynamics simulations. The sulfur-based polymer showed closer packing compared with the other polymers in both pure and blended form. The selenium-based polymer CP3 showed lower free energy of mixing and Flory–Huggins parameter values for various solvents. Our detailed multi-dimensional modelling thus has the potential to assist in the practical design of chalcogen-based polymers for efficient polymer solar cells.

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          Machine learning for high performance organic solar cells: current scenario and future prospects

          In this review, current research status about the machine learning use in organic solar cell research is reviewed. We have discussed the challenges in anticipating the data driven material design. Machine learning (ML) is a field of computer science that uses algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programming methods. Owing to the chemical versatility of organic building blocks, a large number of organic semi-conductors have been used for organic solar cells. Selecting a suitable organic semi-conductor is like searching for a needle in a haystack. Data-driven science, the fourth paradigm of science, has the potential to guide experimentalists to discover and develop new high-performance materials. The last decade has seen impressive progress in materials informatics and data science; however, data-driven molecular design of organic solar cell materials is still challenging. The data-analysis capability of machine learning methods is well known. This review is written about the use of machine learning methods for organic solar cell research. In this review, we have outlined the basics of machine learning and common procedures for applying machine learning. A brief introduction on different classes of machine learning algorithms as well as related software and tools is provided. Then, the current research status of machine learning in organic solar cells is reviewed. We have discussed the challenges in anticipating the data driven material design, such as the complexity metric of organic solar cells, diversity of chemical structures and necessary programming ability. We have also proposed some suggestions that can enhance the usefulness of machine learning for organic solar cell research enterprises.
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            Designing N-phenylaniline-triazol configured donor materials with promising optoelectronic properties for high-efficiency solar cells

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              Molecular engineering of A–D–C–D–A configured small molecular acceptors (SMAs) with promising photovoltaic properties for high-efficiency fullerene-free organic solar cells

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                Author and article information

                Contributors
                Journal
                PCOHC2
                Polymer Chemistry
                Polym. Chem.
                Royal Society of Chemistry (RSC)
                1759-9954
                1759-9962
                November 01 2022
                2022
                : 13
                : 42
                : 5993-6001
                Affiliations
                [1 ]Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials, Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering in Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 100081, China
                [2 ]Research Center for Advanced Materials Science (RCAMS), King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
                [3 ]Department of Chemistry, College of Science, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia
                Article
                10.1039/D2PY00960A
                124a33d5-273e-4900-8970-2aff47c42d85
                © 2022

                http://rsc.li/journals-terms-of-use

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