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Research Article | Open Access

Low-probability events detection using unsupervised multi-prototype clustering for single-molecule electronics

Chi Shang1, Rigong Te1, Shenglun Xiong1, Xipeng Liu1, Taige Lu1, Yixuan Zhu1, Chun Tang1, Jing Li1, Yu Zhou1, Haojie Liu1,2 (), Junyang Liu1 (), Wenjing Hong1 ()
Institution State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering and Institute of Artificial Intelligence and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005, China
Research Center of Grid Energy Storage and Battery Application, School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
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This work proposes a multi-prototype clustering (MPC) algorithm to address the long-term challenge in low-probability events detection for single-molecule electronics.

Abstract

Artificial intelligence for science (AI4S) has emerged as a new horizon in state-of-the-art scientific research, and single-molecule electronics could be considered an ideal prototype in AI4S due to the opportunities in correlating high-throughput and high-quality data with clear physical mechanisms. Towards using artificial intelligence for single-molecule electronics (AI4SME), the unsupervised extraction of low-probability events from the massive experimental data becomes the key step, which has emerged for accurate detection of different configurations and even structural changes in single-molecule junctions. However, the present algorithms suffer from the “uniform effect”, in which the majority events are erroneously allocated to minority ones, resulting in a relatively equal spread of cluster sizes and hindering the investigations for charge transport mechanisms with subtle and complex behaviors in single-molecule electronics. In this work, we propose a new multi-prototype clustering technique for precisely discriminating molecular events during the break junction process, especially those occurring with a probability below 10%, and further precisely extract the product species at the onset of the electric field-driven single-molecule keto-enol reaction with a probability as low as 1.5%. Our work tackles the long-term bottleneck of uniform effect for the precise detection of low-probability single-molecule events.

Keywords

single-molecule electronicslow-probability events detectionmachine learningartificial intelligence for science (AI4S)

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Nano Research
Volume 18 Issue 4,
April 2025
Article number: 94907276
DOI: 10.26599/NR.2025.94907276
Cite this article:
Shang C, Te R, Xiong S, et al. Low-probability events detection using unsupervised multi-prototype clustering for single-molecule electronics. Nano Research, 2025, 18(4): 94907276. https://doi.org/10.26599/NR.2025.94907276
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