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(a) Surface energies for different facets of NiO 2 calculated using metaGGA DFT (blue) and the <t>CHGNet</t> MLIP fine-tuned to the same level of theory (red). The arrow indicates the reduction in surface energy for a (012) facet shown in (b) upon reconstruction to the (001)-like termination shown in (c) together with the evolution <t>of</t> <t>molecular</t> O 2 . Gray and red are Ni–O polyhedra and oxygen ions, respectively. Arrows in (b) indicate the movement of the surface Ni to sites in the Li-layer to form the structure presented in (c).
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(a) Surface energies for different facets of NiO 2 calculated using metaGGA DFT (blue) and the CHGNet MLIP fine-tuned to the same level of theory (red). The arrow indicates the reduction in surface energy for a (012) facet shown in (b) upon reconstruction to the (001)-like termination shown in (c) together with the evolution of molecular O 2 . Gray and red are Ni–O polyhedra and oxygen ions, respectively. Arrows in (b) indicate the movement of the surface Ni to sites in the Li-layer to form the structure presented in (c).

Journal: ACS Applied Materials & Interfaces

Article Title: Probing Surface Degradation Pathways of Charged Nickel-Oxide Cathode Materials Using Machine-Learning Interatomic Potentials

doi: 10.1021/acsami.5c11818

Figure Lengend Snippet: (a) Surface energies for different facets of NiO 2 calculated using metaGGA DFT (blue) and the CHGNet MLIP fine-tuned to the same level of theory (red). The arrow indicates the reduction in surface energy for a (012) facet shown in (b) upon reconstruction to the (001)-like termination shown in (c) together with the evolution of molecular O 2 . Gray and red are Ni–O polyhedra and oxygen ions, respectively. Arrows in (b) indicate the movement of the surface Ni to sites in the Li-layer to form the structure presented in (c).

Article Snippet: ML molecular dynamics simulations have been carried out using Crystal Hamiltonian Graph Neural Network (CHGNet) as the interatomic potential.

Techniques: