Using Weakly Supervised Deep Learning to Classify and Segment Single‐Molecule Break‐Junction Conductance Traces

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...

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Veröffentlicht in:	Chemphyschem 2021-10, Vol.22 (20), p.2107-2114
Hauptverfasser:	Lin, Dongying, Zhao, Zhihao, Pan, Haoyang, Li, Shi, Wang, Yongfeng, Wang, Dong, Sanvito, Stefano, Hou, Shimin
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Classification Conductance-distance trace Deep learning Electronic devices Feature extraction Machine learning Manpower Molecular structure Pretrain-finetune Segments Single-molecule junction Transfer learning
Online-Zugang:	Volltext
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