| Institution: | 1. Department of Electronics, Peking University, Center for Nanoscale Science and Technology, Key Laboratory for the Physics and Chemistry of Nanodevices, Beijing, 100871 China;2. Chinese Academy of Sciences, CAS Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences, Beijing National Laboratory for Molecular Science (BNLMS), Beijing, 100190 China
University of Chinese Academy of Sciences, Beijing, 100049 China;3. School of Physics, Trinity College, AMBER and CRANN Institute, Dublin 2, Ireland |
| Abstract: | In order to design molecular electronic devices with high performance and stability, it is crucial to understand their structure-to-property relationships. Single-molecule break junction measurements yield a large number of conductance-distance traces, which are inherently highly stochastic. Here we propose a weakly supervised deep learning algorithm to classify and segment these conductance traces, a method that is mainly based on transfer learning with the pretrain-finetune technique. By exploiting the powerful feature extraction capabilities of the pretrained VGG-16 network, our convolutional neural network model not only achieves high accuracy in the classification of the conductance traces, but also segments precisely the conductance plateau from an entire trace with very few manually labeled traces. Thus, we can produce a more reliable estimation of the junction conductance and quantify the junction stability. These findings show that our model has achieved a better accuracy-to-manpower efficiency balance, opening up the possibility of using weakly supervised deep learning approaches in the studies of single-molecule junctions. |
