Machine Learning Prediction of the Energy Gap of Graphene Nanoflakes using Topological …

The possibility of band gap engineering in graphene opens countless new opportunities for application in nanoelectronics. In this work, the energy gap of 622 computationally optimized graphene nanoflakes was mapped to topological autocorrelation vectors using machine learning techniques. Machine learning modeling reveals 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 based on their molecular topology.


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