Evidence map›Paper›PMID 41255030›Full record

ArticleSmall (Weinheim an der Bergstrasse, Germany)2026

From Composition to Ionic Conductivity: Machine Learning-Guided Discovery and Experimental Validation of Argyrodite-Type Lithium-Ion Electrolytes.

Songjia Kong, Ziheng Yu, Naoki Matsui, Michiyo Kamiya, Yudai Iwamizu, Yuki Tanaka, Nobuko Kubota, Kuniharu Nomoto, Satoshi Hori, Masaaki Hirayama and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Small (Weinheim an der Bergstrasse, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Songjia KongDepartment 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.ORCID https://orcid.org/0000-0002-3856-9388
Ziheng YuDepartment 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.ORCID https://orcid.org/0009-0009-9543-9051
Naoki MatsuiResearch Center for All-Solid-State Battery, Institute of Integrated Research, Institute of Science Tokyo, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan.
Michiyo KamiyaResearch Center for All-Solid-State Battery, Institute of Integrated Research, Institute of Science Tokyo, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan.
Yudai IwamizuDepartment of Chemical Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8502, Japan.
Yuki TanakaResearch Center for All-Solid-State Battery, Institute of Integrated Research, Institute of Science Tokyo, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan.
Nobuko KubotaResearch Center for All-Solid-State Battery, Institute of Integrated Research, Institute of Science Tokyo, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan.
Kuniharu NomotoResearch Center for All-Solid-State Battery, Institute of Integrated Research, Institute of Science Tokyo, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan.
Satoshi HoriResearch Center for All-Solid-State Battery, Institute of Integrated Research, Institute of Science Tokyo, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan.
Masaaki HirayamaDepartment 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.
Kota SuzukiDepartment 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.ORCID https://orcid.org/0000-0002-2473-0724
Ryoji KannoResearch Center for All-Solid-State Battery, Institute of Integrated Research, Institute of Science Tokyo, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan.

Funding

ALCA-SPRING JPMJAL1301GteX JPMJGX23S5Japan Science and Technology Agency JPMJOP1862Japan Society for the Promotion of Science 24H00042Japan Synchrotron Radiation Research Institute 2025A1754PRESTO JPMJPR17N7
6 · The paper itself

Abstract

The 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 of argyrodite-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. Li

Indexed as

argyrodite‐type electrolytechemical compositionionic conductivitymachine learningsolid‐state electrolyte

Identifiers

PMID41255030
PMCPMC12781620

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.