論文 Periodic table-based compositional descriptors for accelerating electrochemical material discovery: Li-ion conductors and oxygen evolution electrocatalysts

Yen-Ju Wu SAMURAI ORCID ; Yibin Xu SAMURAI ORCID ; Lei Fang SAMURAI ORCID ; Wenqin Peng ; Ken Sakaushi SAMURAI ORCID ; Meiqi Zhang ; Masao Arai SAMURAI ORCID ; Yukinori Koyama SAMURAI ORCID

コレクション

引用
Yen-Ju Wu, Yibin Xu, Lei Fang, Wenqin Peng, Ken Sakaushi, Meiqi Zhang, Masao Arai, Yukinori Koyama. Periodic table-based compositional descriptors for accelerating electrochemical material discovery: Li-ion conductors and oxygen evolution electrocatalysts. Science and Technology of Advanced Materials: Methods. 2025, (), . https://doi.org/10.1080/27660400.2025.2513218

説明:

(abstract)

The discovery of high-performance electrochemical materials is essential for advancing sustainable energy technologies, yet conventional approaches are often limited by trial-and-error experimental processes, time-consuming computations and the need for detailed structural data. To address these challenges, we introduce a periodic table-based compositional descriptor, referred to as the periodic descriptor. Unlike existing methods, our periodic descriptor only requires chemical formulas, making it straightforward and versatile while supporting reversible design, which allows direct conversion between descriptors and chemical compositions.
We applied this approach to two critical applications: fast Li-ion conductors for solid-state electrolytes and platinum-group metal (PGM)-free oxygen evolution reaction (OER) electrocatalysts for water electrolysis. In the case of Li-ion conductors, our model identified both known materials and new candidates, including anti-fluorite structures that exhibit high ionic conductivity at 600-700 K—significantly lower than that of traditional anti-fluorite compounds like Li₂S and Li₂Se. For electrocatalysts, we identified Fe0.1Co0.1Cu0.1Ag0.1W0.6 oxide, which showed electrochemical performance comparable to the benchmark PGM catalyst RuO₂ but at a lower overpotential.
The periodic descriptor demonstrates high predictive accuracy while maintaining low dimensionality, simplifying both the discovery and optimization of materials. This work not only establishes a scalable, efficient framework for material exploration but also highlights the potential for accelerating breakthroughs in green energy solutions, such as next-generation batteries and green hydrogen production, ultimately contributing to carbon neutrality.

権利情報:

キーワード: Solid electrolyte, ionic conductor, electrocatalyst, energy storage, descriptor

刊行年月日: 2025-12-31

出版者: Informa UK Limited

掲載誌:

  • Science and Technology of Advanced Materials: Methods (ISSN: 27660400)

研究助成金:

  • Ministry of Education, Culture, Sports, Science and Technology JPMXP1122712807
  • Center of Innovation Program JPMJPF2016
  • National Institute for Materials Science Materials Open Platform for All Solid-State Batter
  • Advanced Battery Collaboration

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5536

公開URL: https://doi.org/10.1080/27660400.2025.2513218

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更新時刻: 2025-06-18 12:30:21 +0900

MDRでの公開時刻: 2025-06-18 12:20:15 +0900