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• W3206019817 abstract "This work presents a geometry-driven deep learning framework Geometry Orbital of Deep Learning (GOODLE) that accurately predicts all carbon properties in real space and momenta space. We focus on the hybrid carbon orbitals ( sp 2 and sp 3 ) geometry to establish their deep learning “potential”. We demonstrate its excellent performance with the properties predictions in dual space, including energy, equation of states, phonon structure, electronic band structure, and optical absorption. Capable of obtaining the carbon orbital geometries from small lattice carbon structures, GOODLE can be used for both inorganic carbon allotropes and organic hydrocarbon molecules, including the magic-angle twisted bilayer graphene, with excellent transferability. GOODLE is also available for the nudged-elastic band calculation. Besides, we proposed an analytic geometry condition criterial for the carbon allotropes stability. Geometry Orbital of Deep Learning (GOODLE), can accurately predicts all carbon properties in real space and momenta space. We focus on the unique carbon orbital ( sp 2 and sp 3 ) geometry to establish the force field. We demonstrate its excellent performance with the example of properties in dual space, including energy, equation of states, phonon structure, electronic band structure, and optical absorption. With training on small lattice carbon structures, GOODLE can well predict the properties of both inorganic carbon allotropes and organic hydrocarbon molecules, including the magic-angle in twisted bilayer graphene, with excellent transferability." @default.
• W3206019817 created "2021-10-25" @default.
• W3206019817 creator A5013547786 @default.
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• W3206019817 date "2022-01-01" @default.
• W3206019817 modified "2023-10-17" @default.
• W3206019817 title "Geometry Orbital of Deep Learning (GOODLE): A uniform carbon potential" @default.
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• W3206019817 doi "https://doi.org/10.1016/j.carbon.2021.10.043" @default.
• W3206019817 hasPublicationYear "2022" @default.
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