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Structure Prediction of Boron-Doped Graphene by Machine Learning

Structure Prediction of Boron-Doped Graphene by Machine Learning

Thaer M Dieb, Zhufeng Hou, Koji Tsuda

J Chem Phys. 2018 Jun 28;148(24):241716.

PMID: 29960333

Abstract:

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%.

Chemicals Related in the Paper:

Catalog Number	Product Name	Structure	CAS Number	Price
LS76147	Boron-doped graphene			Price

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