Deep learning of deformation-dependent conductance in thin films: Nanobubbles in graphene




Nedell, Jack G.
Spector, Jonah
Abbout, Adel
Vogl, Michael
Fiete, Gregory A.

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Motivated by the ever-improving performance of deep learning techniques, we design a mixed input convolutional neural network approach to predict transport properties in deformed nanoscale materials using a height map of deformations, as can be obtained from scanning probe measure- ments, as input. We employ our approach to study electrical transport in a graphene nanoribbon deformed by a number of randomly positioned nano-bubbles. Our network is able to make con- ductance predictions valid to an average error of 4.3%. We find that such low average errors are achieved by a redundant input of energy values, yielding predictions that are 30-40% more accurate than conventional architectures. We demonstrate that the same method can learn to predict the valley-resolved conductance, with success specifically in identifying the energy at which inter-valley scattering becomes prominent. We demonstrate the robustness of the approach by testing the pre- trained network on samples with deformations differing in number and shape from the training data. We furthermore employ a graph theoretical analysis of the structure and outputs of the network and conclude that a tight-binding Hamiltonian can be effectively encoded in the first layer of the network, which is supported by numerical findings. Our approach contributes a new theoretical understanding and a refined methodology to the application of deep learning for the determination of transport properties based on real-space disorder information.



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Nedell, JG; Spector, J; Abbout, A; Vogl, M; Fiete, GA. Deep learning of deformation-dependent conductance in thin films: Nanobubbles in graphene. Phys. Rev. B 2022, 105(7), 75425-. DOI: 10.1103/PhysRevB.105.075425