Machine Learning-Assisted Optimization of Rb2LiGaI6-Based Perovskite Solar Cells: Insights from SCAPS-1D Simulation

Machine Learning-Assisted Optimization of Rb2LiGaI6-Based Perovskite Solar Cells: Insights from SCAPS-1D Simulation | 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 Machine Learning-Assisted Optimization of Rb 2 LiGaI 6 -Based Perovskite Solar Cells: Insights from SCAPS-1D Simulation Ahnaf Tahmid Abir, Uday Syed, Mohammad Dilwar Ali Alvee, Maruf Kabir Refat, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8916676/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 The development of high-efficiency and stable per- ovskite solar cells (PSCs) requires both suitable material selection and advanced predictive modeling. In this study, Rb 2 LiGaI 6 , a halide perovskite with a bandgap of 1.13 eV, is explored as the absorber material due to its high absorption coefficient ( > 105 cm −1 ), low carrier effective masses (∼0.2–0.4 m 0), and thermodynamic stability. Device architecture optimization was performed using a machine learning (ML) framework to predict photovoltaic performance metrics, including J sc , V oc , FF, and PCE, under varying ETL/HTL interface configurations. Among the tested models, Random Forest achieved the best predictive accuracy R 2 of 0.9974 surpassing XGBoost, HistGBR, and KNN. Correlation and SHAP analyses revealed that HTL bandgap, conduction band offset, electron affinity, and hole mobility play critical roles in governing efficiency. Device simulations showed that absorber thickness strongly influences PCE, to a maximum of 22% at 1000 nm which is very close approximation of the ML predicted PCE of 22.13% with only a prediction error of 0.6%. Optimization of transport layers demonstrated that a thin ETL (∼50 nm) avoids parasitic absorption losses, while moderate HTL thickness (∼100–150 nm) enhances V oc . Temperature- dependent studies further confirmed that device efficiency drops drastically due to elevated dark current. These findings highlight the potential of combining Rb 2 LiGaI 6 absorbers with ML-guided optimization to design efficient and stable PSC architectures. Artificial Intelligence and Machine Learning Materials Chemistry Perovskite solar cells Random Forest Photo- voltaic performance Machine learning Rb2LiGaI6 Full Text Additional Declarations The authors declare no competing interests. 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-8916676","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593852264,"identity":"22518725-bdb1-4cda-b169-5bc1287a09a3","order_by":0,"name":"Ahnaf Tahmid Abir","email":"","orcid":"","institution":"Department of Computer Science and Engineering, East West University, Dhaka, 1212, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Ahnaf","middleName":"Tahmid","lastName":"Abir","suffix":""},{"id":593852265,"identity":"4407a210-d973-4409-8cea-c42f7557e0b3","order_by":1,"name":"Uday Syed","email":"","orcid":"","institution":"Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia","correspondingAuthor":false,"prefix":"","firstName":"Uday","middleName":"","lastName":"Syed","suffix":""},{"id":593852266,"identity":"a4fbe40f-934e-4102-9678-6edaba1528a3","order_by":2,"name":"Mohammad Dilwar Ali Alvee","email":"","orcid":"","institution":"Department of Materials Science and Engineering, Khulna University of Engineering \u0026 Technology, Khulna, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Dilwar Ali","lastName":"Alvee","suffix":""},{"id":593852267,"identity":"0bc03f07-ac7e-48ce-88d9-f544048acb3f","order_by":3,"name":"Maruf Kabir Refat","email":"","orcid":"https://orcid.org/0009-0008-9476-4816","institution":"Department of Information Technology, University of the West of England, Bristol, UK","correspondingAuthor":false,"prefix":"","firstName":"Maruf","middleName":"Kabir","lastName":"Refat","suffix":""},{"id":593852268,"identity":"b19bf70f-aa0c-407b-b196-f8297f409b56","order_by":4,"name":"Md. 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Device architecture optimization was performed using a machine learning (ML) framework to predict photovoltaic performance metrics, including \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eJ\u003c/strong\u003e\u003c/em\u003e\u003cem\u003esc\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eoc\u003c/em\u003e\u003cstrong\u003e, FF, and PCE, under varying ETL/HTL interface configurations. Among the tested models, Random Forest achieved the best predictive accuracy R\u003c/strong\u003e2 \u003cstrong\u003eof 0.9974 surpassing XGBoost, HistGBR, and KNN. Correlation and SHAP analyses revealed that HTL bandgap, conduction band offset, electron affinity, and hole mobility play critical roles in governing efficiency. Device simulations showed that absorber thickness strongly influences PCE, to a maximum of 22% at 1000 nm which is very close approximation of the ML predicted PCE of 22.13% with only a prediction error of 0.6%. Optimization of transport layers demonstrated that a thin ETL (∼50 nm) avoids parasitic absorption losses, while moderate HTL thickness (∼100–150 nm) enhances \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eoc\u003c/em\u003e\u003cstrong\u003e. Temperature- dependent studies further confirmed that device efficiency drops drastically due to elevated dark current. These findings highlight the potential of combining Rb\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003eLiGaI\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e absorbers with ML-guided optimization to design efficient and stable PSC architectures.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Machine Learning-Assisted Optimization of Rb2LiGaI6-Based Perovskite Solar Cells: Insights from SCAPS-1D Simulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 09:55:52","doi":"10.21203/rs.3.rs-8916676/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a762259d-a213-4e00-9be6-2566d4682cc9","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63251216,"name":"Artificial Intelligence and Machine Learning"},{"id":63251217,"name":"Materials Chemistry"}],"tags":[],"updatedAt":"2026-02-20T09:55:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 09:55:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8916676","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8916676","identity":"rs-8916676","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}