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

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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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 measurements, as input. We employ our approach to study electrical transport in a graphene nanoribbon deformed by a number of randomly positioned nanobubbles. Our network is able to make conductance 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 intervalley scattering becomes prominent. We demonstrate the robustness of the approach by testing the pretrained 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 theoretical understanding and a refined methodology to the application of deep learning for the determination of transport properties based on real-space disorder information.

Original languageEnglish
Article number075425
JournalPhysical Review B
Issue number7
StatePublished - 15 Feb 2022

Bibliographical note

Funding Information:
A.A. gratefully acknowledges the support of King Fahd University of Petroleum and Minerals (KFUPM) through Project No. SR191021. M.V. gratefully acknowledges the support provided by the Deanship of Research Oversight and Coordination (DROC) at King Fahd University of Petroleum & Minerals (KFUPM) for funding part of this work through Project No. SR211001. G.A.F. gratefully acknowledges funding from the National Science Foundation through the Center for Dynamics and Control of Materials: An NSF MRSEC under Cooperative Agreement No. DMR-1720595, with additional support from NSF DMR-1949701 and NSF DMR-2114825. This work was performed in part at the Aspen Center for Physics, which is supported by National Science Foundation Grant No. PHY-1607611. This work was also completed in part using the Discovery cluster, supported by Northeastern University's Research Computing team.

Publisher Copyright:
© 2022 American Physical Society.

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics


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