Embodied Intelligence Platform for Materials Synthesis

Embodied Intelligence Platform for Materials Synthesis | 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 Article Embodied Intelligence Platform for Materials Synthesis Xuefeng Yu, Hao Huang, Guolai Jiang, Yutang Li, Boshi Jiang, Yifei Dong, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7161987/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 Artificial intelligence (AI) experimental systems demonstrate marked improvements in material research efficiency. However, challenges in generalization capabilities and experimental operations remain to be addressed. Here, a flexible Embodied Intelligence (EI) platform comprising a home-made EI robot and modular experimental components is designed and demonstrated to overcome these hurdles. The remotely trained EI robot generated action libraries enable consistent, human-like manipulations via encoded trajectories. By integrating large language models-assisted analysis, the synthesis of CsPbI3 quantum dots (QDs) is systematically studied using the EI robot executing key steps. Precise execution of manually uncontrollable manipulations via EI robot directly modulate growth kinetics, demonstrating effects of significance equivalent to conventional reaction parameters. The EI platform thus produces high-quality CsPbI3 QDs with an ultra-narrow full-width at half-maximum of 29.6 nm and a high photoluminescence quantum yield of 76.7%. Two distinct experimental series involving perovskite QDs diversification and extreme-condition synthesis are further conducted to confirm the adaptability and generalization capacity of the EI platform for diverse material systems. This framework facilitates the development of versatile AI systems and enhances the materials research efficiency. Physical sciences/Materials science/Techniques and instrumentation/Design, synthesis and processing Physical sciences/Nanoscience and technology/Techniques and instrumentation/Design, synthesis and processing Physical sciences/Optics and photonics/Optical materials and structures/Quantum dots Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryVideo1.mp4 EI synthesis of CsPbI3 QDs EmbodiedIntelligencePlatformSupplementaryMaterialsHHuang2025.pdf Supplementary Information 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7161987","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":507054476,"identity":"6ddbab48-aeab-45bc-9f0d-b66b1cf7b78c","order_by":0,"name":"Xuefeng 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