Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics-Reference-Cited by-同舟云学术

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Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics

Published:2024 Issue:19 Volume:12 Page:11344-11361
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Miyagawa Takeru¹,Krishnan Namita¹,Grumet Manuel¹,Baecker Christian Reverón¹,Kaiser Waldemar¹^ORCID,Egger David A.¹^ORCID

Affiliation:

1. Department of Physics, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany

Abstract

Machine-learning molecular dynamics provides predictions of structural and anharmonic vibrational properties of solid-state ionic conductors with ab initio accuracy. This opens a path towards rapid design of novel battery materials.

Funder

Gauss Centre for Supercomputing

Deutsche Forschungsgemeinschaft

Alexander von Humboldt-Stiftung

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/TA/D4TA00452C

Reference146 articles.

1. A review of conduction phenomena in Li-ion batteries

2. Electrolytes for solid-state lithium rechargeable batteries: recent advances and perspectives

3. Li–O2 and Li–S batteries with high energy storage

4. Rechargeable Sodium All-Solid-State Battery

5. Recent Progress in Solid Electrolytes for Energy Storage Devices

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Thermal conductivity of Li3PS4 solid electrolytes with ab initio accuracy;Physical Review Materials;2024-06-12

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