Cheng Qian, Daniel Hedman, Pai Li, Sung Youb Kim, Feng Ding
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The Reconstruction of Pt(001) Surface and the Shell-Like Reconstruction of the Vicinal Pt(001) Surfaces Revealed by Neural Network Potential.
In this work, a highly accurate neural network potential (NNP) is presented, named PtNNP, and the exploration of the reconstruction of the Pt(001) surface and its vicinal surfaces with it. Contrary to the most accepted understanding of the Pt(001) surface reconstruction, the study reveals that the main driving force behind Pt(001) quasi-hexagonal reconstruction is not the surface stress relaxation but the increased coordination number of the surface atoms resulting in stronger intralayer binding in the reconstructed surface layer. In agreement with experimental observations, the optimized supercell size of the reconstructed Pt(001) surface contains (5 × 20) unit cells. Surprisingly, the reconstruction of the vicinal Pt(001) surfaces leads to a smooth shell-like surface layer covering the whole surface and diminishing sharp step edges.
期刊介绍：
Small serves as an exceptional platform for both experimental and theoretical studies in fundamental and applied interdisciplinary research at the nano- and microscale. The journal offers a compelling mix of peer-reviewed Research Articles, Reviews, Perspectives, and Comments.
With a remarkable 2022 Journal Impact Factor of 13.3 (Journal Citation Reports from Clarivate Analytics, 2023), Small remains among the top multidisciplinary journals, covering a wide range of topics at the interface of materials science, chemistry, physics, engineering, medicine, and biology.
Small's readership includes biochemists, biologists, biomedical scientists, chemists, engineers, information technologists, materials scientists, physicists, and theoreticians alike.
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