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Machine Learning Prediction of the Energy Gap of Graphene Nanoflakes Using Topological Autocorrelation Vectors.

Abstract：

:The possibility of band gap engineering in graphene opens countless new opportunities for application in nanoelectronics. In this work, the energy gaps of 622 computationally optimized graphene nanoflakes were mapped to topological autocorrelation vectors using machine learning techniques. Machine learning modeling revealed that the most relevant correlations appear at topological distances in the range of 1 to 42 with prediction accuracy higher than 80%. The data-driven model can statistically discriminate between graphene nanoflakes with different energy gaps on the basis of their molecular topology.

journal_name

ACS Comb Sci

journal_title

ACS combinatorial science

authors

Fernandez M,Abreu JI,Shi H,Barnard AS

doi

10.1021/acscombsci.6b00094

subject

Has Abstract

pub_date

2016-11-14 00:00:00

pages

661-664

issue

11

eissn

2156-8952

issn

2156-8944

journal_volume

18

pub_type

杂志文章

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