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Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures.
- Source :
-
Journal of Chemical Physics . 6/25/2018, Vol. 148 Issue 24, pN.PAG-N.PAG. 10p. 1 Diagram, 1 Chart, 6 Graphs. - Publication Year :
- 2018
-
Abstract
- 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 <italic>differences</italic> 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*GAUSSIAN processes
*APPROXIMATION theory
*LITHIUM
*NANOSTRUCTURES
Subjects
Details
- Language :
- English
- ISSN :
- 00219606
- Volume :
- 148
- Issue :
- 24
- Database :
- Academic Search Index
- Journal :
- Journal of Chemical Physics
- Publication Type :
- Academic Journal
- Accession number :
- 130571337
- Full Text :
- https://doi.org/10.1063/1.5016317