SNU CBE

Large-scale screening of high-entropy materials for superionic solid electrolytes

2026 · npj Computational Materials

저자

Junyoung Choi, 정유성 (Yousung Jung)

연구주제

Machine Learning in Materials Science · Advanced Battery Materials and Technologies

NTIS 과학기술표준분류

가장 가까운 분류(재료 > 금속재료 > 구조재료)가 화학·화공 범위 밖이다. 배정 근거: AI 키워드가 잡은 NTIS 과제의 분류.

AI 연구 키워드·방법

고엔트로피 소재 리튬 이온 전도체 superionic solid electrolyte

고속대량 스크리닝(HTS)

키워드 (OpenAlex)

high-entropy materials solid electrolytes superionic conductors lithium-ion conductors ionic conductivity combinatorial materials design halide electrolytes machine learning screening

초록

Solid electrolytes have emerged as a promising alternative to conventional liquid electrolytes that have posed safety concerns. However, the relatively low ionic conductivity of solid electrolytes is one of the challenges for their practical application. In this work, we study high-entropy materials over a wide chemical space as a combinatorial strategy to enhance the ionic conductivity of solid electrolytes by up to orders of magnitude. To this end, we developed an iterative search framework that efficiently identifies chemically compatible substitutional elements to systematically generate 113,098 high-entropy materials, starting from 93 known Li-ion conductors as structural prototypes. By progressively identifying and substituting elements, our scheme enabled efficient and comprehensive coverage of the vast high-entropy compositional space. The generated candidates were then systemati…

이 논문을 지원한 국가R&D 과제 (NTIS 성과)

NTIS 성과DB(2020–24)에서 DOI 매칭 없음

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교수 1

정유성

연구주제 1

Machine Learning in Materials Science

키워드 2

고엔트로피 소재 · 리튬 이온 전도체

방법 1

고속대량 스크리닝(HTS)