Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 148, Issue 24, Pages -Publisher
AMER INST PHYSICS
DOI: 10.1063/1.5016317
Keywords
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Funding
- French Government Scholarship
- Foundation of Ecole des Ponts ParisTech
- Feodor Lynen fellowship from Alexander von Humboldt Foundation
- Leverhulme Early Career Fellowship
- Isaac Newton Trust
- EPSRC [EP/K014560/1]
- Engineering and Physical Sciences Research Council [EP/P022596/1] Funding Source: researchfish
- EPSRC [EP/P022596/1, EP/K014560/1] Funding Source: UKRI
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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. Published by AIP Publishing.
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