Lee, Hyun-Jae Kim, Hyeonjung Ji, Sungyoung Choi, Kyuri Choi, Ho Lim, Woosang Lee, Byungju 2024-10-26T15:30:25Z 2024-10-26T15:30:25Z 2024-10-25 2024-12 1614-6832 https://pubs.kist.re.kr/handle/201004/150887 The introduction of density functional theory (DFT) has improved the study of material properties. This has enabled significant breakthroughs in solid electrolytes, which have emerged as promising candidates for next-generation energy storage systems. However, DFT faces limitations due to the extremely high computational costs required for large atomic cells and long simulation times. In the current study, AI-based simulations using neural network potentials (NNPs) are introduced to extend the capabilities of DFT to explore the effect of anions on lithium diffusion in Li argyrodite (Li6PS5X, X = Cl and Br). The investigation categorizes lithium frameworks into two distinct cages, demonstrating that sulfur ions in these cage centers bind the surrounding lithium ions. From the results, a strategy is proposed to enhance lithium ion conductivity by minimizing the occupation of sulfur ions in cage centers. This research provides a benchmark for evaluating lithium ionic conductivity based on anion configuration in cage centers and advances the understanding of ionic transport in Li argyrodite, informing potential improvements in energy-storage technologies. In current research, it is revealed that lithium ions are localized by highly charged anions such as sulfur ions, significantly impeding lithium transport. Consequently, the lithium ionic conductivity can be enhanced by an effective charge lowering of anions. The investigation also examines previously reported experimental results that demonstrate the theoretical predictions based on machine learning. image English Wiley-VCH Verlag Lithium Localization by Anions in Argyrodite Solid Electrolytes from Machine-Learning-based Simulations Article 10.1002/aenm.202402396 1 Advanced Energy Materials, v.14, no.48 Advanced Energy Materials 14 48 N scie scopus 001329938700001 2-s2.0-85205886506 Chemistry, Physical Energy & Fuels Materials Science, Multidisciplinary Physics, Applied Physics, Condensed Matter Chemistry Energy & Fuels Materials Science Physics Article ENCODING CRYSTAL-STRUCTURE CUBIC LI-ARGYRODITES ION CONDUCTION LI6PS5X X BATTERIES DYNAMICS ENERGY PREDICTION EFFICIENT DISORDER argyrodite battery computational material science neural network potential solid electrolyte