journal article·2024-12Open access

Lithium Localization by Anions in Argyrodite Solid Electrolytes from Machine‐Learning‐based Simulations (Adv. Energy Mater. 48/2024)

Hyun‐Jae Lee, Hyeonjung Kim, Sungyoung Ji, Kyuri Choi, Ho Choi, Woosang Lim, Byungju Lee

Advanced Energy Materials· Volume 14· Issue 48

Abstract

Solid Electrolytes In article number 2402396, Byungju Lee and co-workers employ artificial intelligence (AI) to investigate lithium-ion transport within solid electrolytes using large-scale molecular dynamics simulations. By integrating AI-driven insights, it provides a detailed understanding of ion dynamics, aiming to enhance the performance of lithium-ion batteries and guide the development of next-generation solid-state energy storage systems. This approach offers a novel pathway for optimizing battery efficiency.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Battery Materials and Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Computational Science Research Center Korea Institute of Science and Technology (KIST) 5 Hwarangno 14‐gil, Seongbuk‐gu Seoul 02792 Republic of Korea

Applied AI Research Team POSCO HOLDINGS Teheran‐ro, Gangnam‐gu Seoul 06194 Republic of Korea

References

Reference metadata was not supplied for this record.