Literature DB >> 33685134

Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors.

Jianxing Huang1, Linfeng Zhang2, Han Wang3, Jinbao Zhao1, Jun Cheng1, Weinan E2.   

Abstract

Solid-state electrolyte materials with superior lithium ionic conductivities are vital to the next-generation Li-ion batteries. Molecular dynamics could provide atomic scale information to understand the diffusion process of Li-ion in these superionic conductor materials. Here, we implement the deep potential generator to set up an efficient protocol to automatically generate interatomic potentials for Li10GeP2S12-type solid-state electrolyte materials (Li10GeP2S12, Li10SiP2S12, and Li10SnP2S12). The reliability and accuracy of the fast interatomic potentials are validated. With the potentials, we extend the simulation of the diffusion process to a wide temperature range (300 K-1000 K) and systems with large size (∼1000 atoms). Important technical aspects such as the statistical error and size effect are carefully investigated, and benchmark tests including the effect of density functional, thermal expansion, and configurational disorder are performed. The computed data that consider these factors agree well with the experimental results, and we find that the three structures show different behaviors with respect to configurational disorder. Our work paves the way for further research on computation screening of solid-state electrolyte materials.

Entities:  

Year:  2021        PMID: 33685134     DOI: 10.1063/5.0041849

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  3 in total

1.  Paradigms of frustration in superionic solid electrolytes.

Authors:  Brandon C Wood; Joel B Varley; Kyoung E Kweon; Patrick Shea; Alex T Hall; Andrew Grieder; Michaele Ward; Vincent P Aguirre; Dylan Rigling; Eduardoe Lopez Ventura; Chimara Stancill; Nicole Adelstein
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2021-10-11       Impact factor: 4.226

2.  A machine learning protocol for revealing ion transport mechanisms from dynamic NMR shifts in paramagnetic battery materials.

Authors:  Min Lin; Jingfang Xiong; Mintao Su; Feng Wang; Xiangsi Liu; Yifan Hou; Riqiang Fu; Yong Yang; Jun Cheng
Journal:  Chem Sci       Date:  2022-06-13       Impact factor: 9.969

3.  Thermodynamics and Kinetics of the Cathode-Electrolyte Interface in All-Solid-State Li-S Batteries.

Authors:  Manas Likhit Holekevi Chandrappa; Ji Qi; Chi Chen; Swastika Banerjee; Shyue Ping Ong
Journal:  J Am Chem Soc       Date:  2022-09-23       Impact factor: 16.383

  3 in total

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