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Guided by E2I, Si–Sn, Ge–Si, and Ge–Sn co-substituted argyrodites are synthesized, reaching a maximum conductivity of 7.2 × 10−3 S cm−1 for Li6.7Ge0.595Si0.105P0.3S5I with low activation energy. Hot-pressing optimization yields values comparable to LGPS-class superionic conductors while reducing experimental workload, improving identification across high- and low-conductivity regions within complex chemical spaces.",{"@graph":14,"@context":76},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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compositions.",{"name":69,"@type":60,"acceptedAnswer":70},"Which material and performance metrics did the study report as the best results?",{"text":71,"@type":63},"Li6.7Ge0.595Si0.105P0.3S5I achieved the highest ionic conductivity (7.2 × 10−3 S cm−1) with a low activation energy of 0.20 eV.",{"name":73,"@type":60,"acceptedAnswer":74},"What experimental approach was used to further enhance conductivity after synthesis?",{"text":75,"@type":63},"The study used hot-pressing to optimize the conductivity, reaching values comparable to those of LGPS-type superionic conductors (over 10−2 S cm−1).","https://schema.org",{"og:url":32,"og:type":78,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":80,"canonical":32},"index,follow",{"doc_id":82,"site_id":7},457692,1790790664,{"code":4,"msg":85,"data":86},"success",[87,91,95,99,104,109,114,118,123,126,130],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Story & 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Learning-Guided Discovery and Experimental Validation of Argyrodite-Type Lithium-Ion Electrolytes  \nSongjia Kong, Ziheng Yu, Naoki Matsui, Michiyo Kamiya, Yudai Iwamizu, Yuki Tanaka, Nobuko Kubota, Kuniharu Nomoto, Satoshi Hori, Masaaki Hirayama, Kota Suzuki,* and Ryoji Kanno*  \nThe discovery of solid-state electrolytes (SSEs) with high lithium-ion conductivities is critical for advancing all-solid-state batteries. However, prior efforts have largely focused on structure-driven design. This study presents a composition-based machine learning framework, Elements-To-Ionics (E2I), for the accurate prediction and optimization of the ionic conductivities ofargyrodite-type SSEs using only their elemental compositions. Guided by these predictions, a series of Si–Sn, Ge–Si, and Ge–Sn co-substituted argyrodites are synthesized. Li6.7 Ge0.595Si0.105 P0.3S5 I achieves the highest ionic conductivity (7.2 × 10−3 S cm−1) with a low activation energy (0.20 eV). Using hot-pressing to optimize the conductivity, values comparable to those of Li10 GeP2S12-type superionic conductors are achieved ( >10−2 S cm−1). The developed model reliably identifies both high-and low-conductivity regions and significantly reduces the experimental workload. These results highlight the potential of composition-based informatics for accelerating the discovery of high-performance SSEs within complex chemical spaces, and provide a valuable methodology for the development of next-generation solid-state battery technologies.  \nowing to their intrinsic safety profiles, high energy densities, and potential compatibility with lithium metal anodes. [1] Among the various classes of solid-state electrolytes (SSEs) reported to date, sulfide-based materials have received particular attention due to their high ionic conductivities, soft mechanical properties, and favorable interface characteristics. [2,3] However, despite remarkable progress in this field, many currently known sulfide SSEs still fall short of combining wide electrochemical windows with high conductivities and longterm stability characteristics. [4] As a result, the search for novel lithium-ion conducting sulfide materials remains an active area of research.  \nIn the conventional development of SSEs, many impressive materials that exhibit high ionic conductivities have been discovered. In particular, systematic cation and anion substitutions have enabled significant enhancements in the lithiumion conductivities of such materials. For  \n1. Introduction  \nAll-solid-state lithium-ion batteries (ASSLIBs) are widely regarded as a promising next-generation energy storage technology  \ninstance, starting from the ternary Li-P-S system, the partial substitution of P with Ge produced the well-known superionic conductor Li10 GeP2 S12 (LGPS), which exhibits an extremely high ionic conductivity of ≈10−2 S cm−1 . [5] Subsequent modifications,  \nS. Kong, Z. Yu, M. Hirayama, K. Suzuki Department of Chemical Science and Engineering School of Materials and Chemical Technology Institute of Science Tokyo  \n4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan  \nE-mail: [suzuki.k.f71a@m.isct.ac.jp](suzuki.k.f71a@m.isct.ac.jp)  \nThe ORCID identification number(s) for the author(s) of this article can be found under [https://doi.org/10.1002/smll.202509918](https://doi.org/10.1002/smll.202509918)  \n© 2025 The Author(s) . Small published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.  \nDOI: 10.1002/smll.202509918  \nN. Matsui, M. Kamiya, Y. Tanaka, N. Kubota, K. Nomoto, S. Hori,  \nK. Suzuki, R. Kanno  \nResearch Center for All-Solid-State Battery Institute of Integrated Res","cbCait9kGKRQXeku","https://ap.wps.com/l/cbCait9kGKRQXeku","pdf",2171165,"English","# Introduction\n## Solid-state lithium-ion batteries and the role of solid-state electrolytes\n## Limits of prior structure-driven design\n## Composition-based machine learning framework (Elements-To-Ionics, E2I)\n## Co-substituted argyrodite synthesis and conductivity optimization\n## Outcomes and implications for next-generation batteries","[{\"question\":\"What problem does the study address in designing solid-state electrolytes?\",\"answer\":\"It targets the need for solid-state electrolytes with high lithium-ion conductivities, noting that prior efforts mainly relied on structure-driven design and still struggled to achieve the best combination of conductivity and stability.\"},{\"question\":\"How does the Elements-To-Ionics (E2I) framework work?\",\"answer\":\"E2I predicts and optimizes ionic conductivities of argyrodite-type electrolytes using only their elemental compositions, enabling guidance for selecting promising compositions.\"},{\"question\":\"Which material and performance metrics did the study report as the best results?\",\"answer\":\"Li6.7Ge0.595Si0.105P0.3S5I achieved the highest ionic conductivity (7.2 × 10−3 S cm−1) with a low activation energy of 0.20 eV.\"},{\"question\":\"What experimental approach was used to further enhance conductivity after synthesis?\",\"answer\":\"The study used hot-pressing to optimize the conductivity, reaching values comparable to those of LGPS-type superionic conductors (over 10−2 S cm−1).\"}]","From Composition to Ionic Conductivity - Machine Learning-Guided Discovery and Experimental Validation of Argyrodite-Type Lithium-Ion Electrolytes | PDF",1790750175,25]