Classification of conductance traces with recurrent neural networks

The Journal of Chemical Physics
Kasper P LauritzenGemma C Solomon

Abstract

We present a new automated method for structural classification of the traces obtained in break junction experiments. Using recurrent neural networks trained on the traces of minimal cross-sectional area in molecular dynamics simulations, we successfully separate the traces into two classes: point contact or nanowire. This is done without any assumptions about the expected features of each class. The trained neural network is applied to experimental break junction conductance traces, and it separates the classes as well as the previously used experimental methods. The effect of using partial conductance traces is explored, and we show that the method performs equally well using full or partial traces (as long as the trace just prior to breaking is included). When only the initial part of the trace is included, the results are still better than random chance. Finally, we show that the neural network classification method can be used to classify experimental conductance traces without using simulated results for training, but instead training the network on a few representative experimental traces. This offers a tool to recognize some characteristic motifs of the traces, which can be hard to find by simple data selection algorithms.

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Citations

Apr 3, 2020·Nanoscale·András MagyarkutiAndrás Halbritter
Dec 29, 2020·Nanotechnology·Yan Qi HuanChit Siong Lau
Jul 30, 2021·Chemphyschem : a European Journal of Chemical Physics and Physical Chemistry·Dongying LinShimin Hou
Apr 4, 2020·Nano Letters·Tianren FuLatha Venkataraman
Jul 16, 2020·The Journal of Physical Chemistry Letters·Bo LiuMasateru Taniguchi
Sep 3, 2020·The Journal of Physical Chemistry. a·Saientan BagPrabal K Maiti
Nov 2, 2021·The Journal of Physical Chemistry Letters·Tianren FuLatha Venkataraman

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