Machine Learning Prediction of the Energy Gap of Graphene Nanoflakes Using Topological Autocorrelation Vectors.
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.
Data61, CSIRO , 343 Royal Parade, Parkville, Victoria 3052, Australia.
Michael
M
Fernandez
Departamento de Ingeniería Informática, Facultad de Ingeniería, Universidad Católica de la Santísima Concepción , Alonso de Ribera 2850, Concepción, Chile.
Jose I
JI
Abreu
Data61, CSIRO , 343 Royal Parade, Parkville, Victoria 3052, Australia.
Hongqing
H
Shi
Data61, CSIRO , 343 Royal Parade, Parkville, Victoria 3052, Australia.
Amanda S
AS
Barnard
D004735:Energy Transfer; D006108:Graphite / Q000737:chemistry*; D000069550:Machine Learning; D008958:Models, Molecular; D015394:Molecular Structure; D049329:Nanostructures / Q000737:chemistry*
D006108:Graphite
band gap engineering; graphene; machine learning; molecular topology; topological autocorrelation vectors