Lithium Interaction with Graphene Materials at Finite Temperature
ORAL
Abstract
The increasing demand for high energy density lithium ion batteries motivates a search for alternative electrode materials.
Experimentally obtained graphene based structures have been suggested to replace the state-of-the-art graphitic anode [1]
We characterized the Li adsorption on graphene both at zero and finite temperatures. The zero temperature study was carried out by means of density functional theory (DFT) accounting for van der Waals interactions while the finite temperature behavior was studied by Monte Carlo techniques with DFT-derived Li-graphene interaction potential constructed via cluster expansion method. At zero temperature, the dispersed Li configurations are unstable with respect to metallic Li. At higher temperatures, entropic effects stabilize lower concentrations with respect to bulk Li while below 400\,K, the formation of 2D Li-clusters is stable over a random distribution of Li. In order to understand the nature of Li interactions with all carbon materials rather than single layer graphene, we are developing an artificial-neural-network based lithium-carbon interaction potential employing Behler and Parrinello symmetry functions[2] as structural descriptors. This will allow a detailed investigation and characterization of Li interaction with these materials.
Experimentally obtained graphene based structures have been suggested to replace the state-of-the-art graphitic anode [1]
We characterized the Li adsorption on graphene both at zero and finite temperatures. The zero temperature study was carried out by means of density functional theory (DFT) accounting for van der Waals interactions while the finite temperature behavior was studied by Monte Carlo techniques with DFT-derived Li-graphene interaction potential constructed via cluster expansion method. At zero temperature, the dispersed Li configurations are unstable with respect to metallic Li. At higher temperatures, entropic effects stabilize lower concentrations with respect to bulk Li while below 400\,K, the formation of 2D Li-clusters is stable over a random distribution of Li. In order to understand the nature of Li interactions with all carbon materials rather than single layer graphene, we are developing an artificial-neural-network based lithium-carbon interaction potential employing Behler and Parrinello symmetry functions[2] as structural descriptors. This will allow a detailed investigation and characterization of Li interaction with these materials.
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Presenters
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Yusuf Shaidu
Condensed Matter Physics, International School for Advanced Studies, International School for Advanced Studies
Authors
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Yusuf Shaidu
Condensed Matter Physics, International School for Advanced Studies, International School for Advanced Studies
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Emine Kucukbenli
Condensed Matter Physics, International School for Advanced Studies, International School for Advanced Studies
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Stefano de Gironcoli
Condensed Matter Physics, International School for Advanced Studies, International School for Advanced Studies