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journal article·2026-01Open access

From Composition to Ionic Conductivity: Machine Learning‐Guided Discovery and Experimental Validation of Argyrodite‐Type Lithium‐Ion Electrolytes (Small 2/2026)

Songjia Kong, Ziheng Yu, Naoki Matsui, Michiyo Kamiya, Yudai Iwamizu, Yuki Tanaka, Nobuko Kubota, Kuniharu Nomoto, Satoshi Hori, Masaaki Hirayama, Kota Suzuki, Ryoji Kanno

Small· Volume 22· Issue 2
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Source abstract

Abstract

Lithium-Ion Electrolytes The cover illustrates the composition-based machine learning framework Elements-To-Ionics (E2I) for predicting and optimizing ionic conductivities of argyrodite-type solid-state electrolytes. Guided by E2I predictions, in article number 2509918, Kota Suzuki, Ryoji Kanno, and co-workers successfully explored Si–Sn, Ge–Si, and Ge–Sn co-doped Li6PS5I systems, achieving ionic conductivities comparable to superionic conductors. This work highlights the potential of composition-based informatics for accelerating next-generation solid-state battery development.
Source: openalex_inverted_index · Quality: baseline

Research context

Energy storage

OpenAlex topics: Machine Learning in Materials Science · Advanced Battery Materials and Technologies · Inorganic Chemistry and Materials

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Affiliations

Department of Chemical Science and Engineering School of Materials and Chemical Technology Institute of Science Tokyo 4259 Nagatsuta‐cho, Midori‐ku Yokohama Kanagawa 226‐8501 Japan

Research Center for All‐Solid‐State Battery Institute of Integrated Research Institute of Science Tokyo 4259 Nagatsuta‐cho, Midori‐ku Yokohama Kanagawa 226‐8501 Japan

Department of Chemical Science and Engineering School of Materials and Chemical Technology Tokyo Institute of Technology 4259 Nagatsuta‐cho, Midori‐ku Yokohama 226‐8502 Japan

References

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