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説明:

Heteroatom doping has endowed graphene with manifold aspects of material properties and boosted its applications. The atomic structure determination of doped graphene is vital to understand its material properties. Motivated by the recently synthesized boron-doped graphene with relatively high concentration, here we employ machine learning methods to search the most stable structures of doped boron atoms in graphene, in conjunction with the atomistic simulations. From the determined stable structures, we find that in the free-standing pristine graphene, the doped boron atoms energetically prefer to substitute for the carbon atoms at different sublattice sites and that the para configuration of boron-boron pair is dominant in the cases of high boron concentrations. The boron doping can increase the work function of graphene by 0.7 eV for a boron content higher than 3.1%

権利情報:

キーワード: Materials design, Monte Carlo tree search

刊行年月日: 2018-06-28

出版者: AIP Publishing

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研究助成金:

原稿種別: 査読前原稿 (Author's original)

MDR DOI:

公開URL: https://doi.org/10.1063/1.5018065

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更新時刻: 2024-01-05 22:12:04 +0900

MDRでの公開時刻: 2021-08-14 03:55:17 +0900