High-throughput computational screening for solid-state Li-ion conductors

DOI

We present a computational screening of experimental structural repositories for fast Li-ion conductors, with the goal of finding new candidate materials for application as solid-state electrolytes in next-generation batteries. We start from ~1400 unique Li-containing materials, of which ~900 are insulators at the level of density-functional theory. For those, we calculate the diffusion coefficient in a highly automated fashion, using extensive molecular dynamics simulations on a potential energy surface (the recently published pinball model) fitted on first-principles forces. The ~130 most promising candidates are studied with full first-principles molecular dynamics, first at high temperature and then more extensively for the 78 most promising candidates. The results of the first-principles simulations of the candidate solid-state electrolytes found are discussed in detail.

Update April 2024: Files are added that facilitate the Materials Cloud Archive OPTIMADE service to serve the structural data of this Archive entry via an OPTIMADE API. The molecular dynamics trajectories are served as individual structures per time-step.

Identifier
DOI https://doi.org/10.24435/materialscloud:vg-ya
Related Identifier https://arxiv.org/abs/1909.00623
Related Identifier https://doi.org/10.1039/C9EE02457C
Related Identifier https://pubs.rsc.org/en/content/articlelanding/2020/ee/c9ee02457c
Related Identifier https://renkulab.io/projects/new?data=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
Related Identifier https://optimade.materialscloud.org/archive/vg-ya
Related Identifier https://archive.materialscloud.org/communities/mcarchive
Related Identifier https://doi.org/10.24435/materialscloud:jk-9v
Metadata Access https://archive.materialscloud.org/oai2d?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:2158
Provenance
Creator Kahle, Leonid; Marcolongo, Aris; Marzari, Nicola
Publisher Materials Cloud
Contributor Kahle, Leonid; Marzari, Nicola
Publication Year 2024
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type info:eu-repo/semantics/other
Format text/plain; application/octet-stream; application/gzip; text/markdown
Discipline Materials Science and Engineering