CUED Publications database

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

Fujikake, S and Deringer, VL and Lee, TH and Krynski, M and Elliott, SR and Csányi, G (2018) Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures. J Chem Phys, 148. p. 241714. ISSN 0021-9606

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We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density functional theory data. Rather than treating the full Li-C system, we demonstrate how the energy and force differences arising from Li intercalation can be modeled and then added to a (prexisting and unmodified) GAP model of pure elemental carbon. Furthermore, we show the benefit of using an explicit pair potential fit to capture "effective" Li-Li interactions and to improve the performance of the GAP model. This provides proof-of-concept for modeling guest atoms in host frameworks with machine-learning based potentials and in the longer run is promising for carrying out detailed atomistic studies of battery materials.

Item Type: Article
Uncontrolled Keywords: cond-mat.mtrl-sci cond-mat.mtrl-sci
Divisions: Div C > Applied Mechanics
Div C > Materials Engineering
Depositing User: Cron Job
Date Deposited: 27 Dec 2017 20:06
Last Modified: 02 Mar 2021 06:54
DOI: 10.1063/1.5016317