Probing Surface Degradation Pathways of Charged Nickel-Oxide Cathode Materials Using Machine-Learning Interatomic Potentials.

Svenja Both, Andrey D Poletayev, Timo Danner, Arnulf Latz, M Saiful Islam

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Article in ACS applied materials & interfaces, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Svenja BothGerman Aerospace Center, Institute of Engineering Thermodynamics, Ulm 89081, Germany.ORCID 0009-0000-0748-304X

Andrey D PoletayevDepartment of Materials, University of Oxford, Oxford OX1 3PH, U.K.ORCID 0000-0002-7892-8963

Timo DannerGerman Aerospace Center, Institute of Engineering Thermodynamics, Ulm 89081, Germany.ORCID 0000-0003-2336-6059

Arnulf LatzGerman Aerospace Center, Institute of Engineering Thermodynamics, Ulm 89081, Germany.ORCID 0000-0003-1449-8172

M Saiful IslamDepartment of Materials, University of Oxford, Oxford OX1 3PH, U.K.ORCID 0000-0003-3882-0285

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While nickel-based layered oxide cathodes offer promising energy and power densities in lithium-ion batteries, they suffer from instability when fully delithiated upon charge. Ex situ studies often report a structural degradation of the charged cathode materials, but the precise mechanism is still poorly understood on the atomic scale. In this work, we combine high-level ab initio calculations with molecular dynamics using machine-learning interatomic potentials to study structural degradation of fully delithiated LiNiO

Indexed as

density-functional theoryLi-ion batteriesmachine-learning interatomic potentialsNi-rich cathode materialssurface degradation

Identifiers

PMID40994259

DOI10.1021/acsami.5c11818

PMCPMC12516693

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