[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124093-en":3,"doc-seo-124093-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124093,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Rapid Data-Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion","Halide perovskite nanocrystals require extensive trial-and-error to tune composition and synthesis, yet most machine-learning optimization methods need large input datasets. This work fuses three established machine-learning models with Bayesian optimization to optimize CsPbBr3 nanoplatelet syntheses under limited data demands. Using only three precursor ratios, the approach predicts photoluminescence emission maxima and enables previously unattainable seven- and eight-monolayer-thick nanoplatelets. It also greatly improves dispersion homogeneity for 2–6 monolayers, achieving the result with just 200 total syntheses and extending readily to other nanocrystal systems.","ReseaRch aRticle  \n[www.advmat.de](www.advmat.de)  \nRapid Data-Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion  \nCarola Lampe, Ioannis Kouroudis, Milan Harth, Stefan Martin, Alessio Gagliardi, and Alexander S. Urban*  \nWith the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and error process. Recently, machine learning has emerged as a powerful tool to help optimize syntheses; however, most approaches require a substantial amount of input data, limiting their pertinence. Here, three well-known machine-learning models are merged with Bayesian optimization into one to optimize the synthesis of CsPbBr3 nanoplatelets with limited data demand. The algorithm can accurately predict the photoluminescence emission maxima of nanoplatelet dispersions using only the three precursor ratios as input parameters. This allows us to fabricate previously unobtainable seven and eight monolayer-thick nanoplatelets. Moreover, the algorithm dramatically improves the homogeneity of 2–6-monolayer-thick nanoplatelet dispersions, as evidenced by narrower and more symmetric photoluminescence spectra. Decisively, only 200 total syntheses are required to achieve this vast improvement, highlighting how rapidly material properties can be optimized. The algorithm is highly versatile and can incorporate additional synthetic parameters. Accordingly, it is readily applicable to other less-explored nanocrystal syntheses and can help rapidly identify and improve exciting compositions’ quality.  \n1. Introduction  \nHalide perovskite nanocrystals (PNCs), first demonstrated in 2014, have been rapidly improved, yielding tunability throughout the visible spectrum, quantum yields approaching 100%, and diverse geometries and sizes. [1] Due to their exceptional properties, PNCs have already been incorporated into diverse applications, focusing on optoelectronics such as LEDs, solar cells, and photodetectors, but also in field-effect transistors and, even more recently, photocatalysis. [2–6] Despite these impressive improvements, several issues impede widespread commercialization, such as stability, lead toxicity, and spectral efficiency in the blue region of the visible spectrum. [7–9] This latter effect is due to the chloride-perovskites being far from defect tolerant, resulting in extremely poor efficiencies compared to bromide- and iodide-based perovskites. [10] Another way to tune the spectral response in PNCs is through quantum confinement. Especially, 2D nanoplatelets (NPLs) are ideal in this regard, as they exhibit no inhomogeneous broadening in the confined dimension,  \nC. Lampe, S. Martin, A. S. Urban  \nNanospectroscopy Group and Center for NanoScience Nano-Institute Munich  \nFaculty of Physics  \nLudwig-Maximilians-Universität München  \n80539 Munich, Germany E-mail: [urban@lmu.de](urban@lmu.de)  \nI. Kouroudis, M. Harth, A. Gagliardi  \nDepartment of Electrical and Computer Engineering Technical University of Munich  \nHans-Piloty-Straße 1, 85748 Garching bei München, Germany The ORCID identification number(s) for the author(s) of this article can be found under [https://doi.org/10.1002/adma.202208772](https://doi.org/10.1002/adma.202208772) .  \n© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.  \nDOI: 10.1002/adma.202208772  \nwith only incremental thickness values possible—currently between two and six monolayers (MLs) . [11] Analogous to the bulk-like Ruddlesden–Popper perovskites,[12] their strong confinement c","cbCail6LrLTCSXp6","https://ap.wps.com/l/cbCail6LrLTCSXp6","pdf",1712529,1,"English","en",105,"# Introduction\n## Machine learning for synthesis optimization\n## Perovskite nanocrystals and 2D nanoplatelets\n## Data efficiency and Bayesian optimization integration","[{\"question\":\"Why is synthesis optimization for perovskite nanocrystal nanoplatelets difficult with experiments alone?\",\"answer\":\"It involves a vast number of composition and fabrication parameters, making purely intuition-driven optimization tedious and infeasible.\"},{\"question\":\"How does the proposed machine-learning approach optimize CsPbBr3 nanoplatelet synthesis with limited data?\",\"answer\":\"It merges three well-known machine-learning models with Bayesian optimization, using only three precursor ratios as inputs to predict photoluminescence emission maxima.\"},{\"question\":\"What improvement does the algorithm deliver for nanoplatelet dispersions?\",\"answer\":\"It enables seven- and eight-monolayer-thick nanoplatelets and improves homogeneity for 2–6 monolayers, reflected in narrower and more symmetric photoluminescence spectra achieved with about 200 total syntheses.\"}]","Rapid Data-Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion | 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is synthesis optimization for perovskite nanocrystal nanoplatelets difficult with experiments alone?","Question",{"text":74,"@type":75},"It involves a vast number of composition and fabrication parameters, making purely intuition-driven optimization tedious and infeasible.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed machine-learning approach optimize CsPbBr3 nanoplatelet synthesis with limited data?",{"text":79,"@type":75},"It merges three well-known machine-learning models with Bayesian optimization, using only three precursor ratios as inputs to predict photoluminescence emission maxima.",{"name":81,"@type":72,"acceptedAnswer":82},"What improvement does the algorithm deliver for nanoplatelet dispersions?",{"text":83,"@type":75},"It enables seven- and eight-monolayer-thick nanoplatelets and improves homogeneity for 2–6 monolayers, reflected in narrower and more symmetric photoluminescence spectra achieved with about 200 total 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