Accelerated Computational Discovery of High-Performance Materials for Organic Photovoltaics by Means of Cheminformatics

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Accelerated Computational Discovery of High-Performance Materials for Organic Photovoltaics by Means of Cheminformatics

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dc.contributor.author Olivares-Amaya, Roberto
dc.contributor.author Amador-Bedolla, Carlos
dc.contributor.author Hachmann, Johannes
dc.contributor.author Atahan-Evrenk, Sule
dc.contributor.author Sánchez-Carrera, Roel S.
dc.contributor.author Vogt, Leslie
dc.contributor.author Aspuru-Guzik, Alán
dc.date.accessioned 2012-04-06T19:23:33Z
dc.date.issued 2011
dc.identifier.citation Olivares-Amaya, Roberto, Carlos Amador-Bedolla, Johannes Hachmann, Sule Atahan-Evrenk, Roel S. Sánchez-Carrera, Leslie Vogt, and Alán Aspuru-Guzik. 2011. Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics. Energy and Environmental Science 4(12): 4849–4861. en_US
dc.identifier.issn 1754-5692 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:8519265
dc.description.abstract In this perspective we explore the use of strategies from drug discovery, pattern recognition, and machine learning in the context of computational materials science. We focus our discussion on the development of donor materials for organic photovoltaics by means of a cheminformatics approach. These methods enable the development of models based on molecular descriptors that can be correlated to the important characteristics of the materials. Particularly, we formulate empirical models, parametrized using a training set of donor polymers with available experimental data, for the important current–voltage and efficiency characteristics of candidate molecules. The descriptors are readily computed which allows us to rapidly assess key quantities related to the performance of organic photovoltaics for many candidate molecules. As part of the Harvard Clean Energy Project, we use this approach to quickly obtain an initial ranking of its molecular library with 2.6 million candidate compounds. Our method reveals molecular motifs of particular interest, such as the benzothiadiazole and thienopyrrole moieties, which are present in the most promising set of molecules. en_US
dc.description.sponsorship Chemistry and Chemical Biology en_US
dc.language.iso en_US en_US
dc.publisher Royal Society of Chemistry en_US
dc.relation.isversionof doi:10.1039/c1ee02056k en_US
dash.license OAP
dc.title Accelerated Computational Discovery of High-Performance Materials for Organic Photovoltaics by Means of Cheminformatics en_US
dc.type Journal Article en_US
dc.description.version Author's Original en_US
dc.relation.journal Energy and Environmental Science en_US
dash.depositing.author Aspuru-Guzik, Alán
dc.date.available 2012-04-06T19:23:33Z

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  • FAS Scholarly Articles [7219]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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