Multi-objective AI-driven optimization guides the discovery of high-performance organic photovoltaics

Multi-objective AI-driven optimization guides the discovery of high-performance organic photovoltaics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-objective AI-driven optimization guides the discovery of high-performance organic photovoltaics Feiyue Lu, Kang An, Qin Wang, Zhipeng Yin, Xinhui Liu, Jialin Wu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9208372/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Experience-driven optimization has been pivotal in advancing organic photovoltaics, where chemically intuitive and stepwise tuning of processing parameters can yield high-performance organic solar cells (OSCs). However, systematically locating optimal regions under high-dimensional, strongly coupled, and multi-objective constraints remains challenging across diverse scenarios. Here, we introduce a closed-loop workflow base on multi-objective Bayesian optimization for efficient process optimization of OSCs. Applied to a quaternary system comprising the widely used polymer donors PM6 and D18 and the non-fullerene acceptors BTP-eC9 and L8BO, the workflow navigates an eight-dimensional space jointly defined by composition and fabrication variables, encompassing 2.2 × 10 14 possible combinations. Within five active-learning cycles, the search rapidly converges to a high-performance region, achieving a power conversion efficiency exceeding 20% while drastically reducing the time and effort compared to conventional trial-and-error approaches. The generalizability of the workflow is further demonstrated across different material systems and fabrication methods, consistently guiding the optimization toward high-performance regions in only a few iterations. This work establishes a generalizable, efficient, and scalable closed-loop framework for the rapid identification of optimal process windows in high-dimensional, multi-objective systems for organic photovoltaics and beyond. Organic solar cells Multi-objective Bayesian optimization Process optimization Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9208372","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612017605,"identity":"1d6dd120-f32c-4401-b63a-3185b9bf3bf0","order_by":0,"name":"Feiyue Lu","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Feiyue","middleName":"","lastName":"Lu","suffix":""},{"id":612017606,"identity":"31489cd3-7e13-45ae-976d-3376ef11566b","order_by":1,"name":"Kang An","email":"","orcid":"","institution":"South China University of 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