@article{Shang2025, 
author = {Chi Shang and Rigong Te and Shenglun Xiong and Xipeng Liu and Taige Lu and Yixuan Zhu and Chun Tang and Jing Li and Yu Zhou and Haojie Liu and Junyang Liu and Wenjing Hong},
title = {Low-probability events detection using unsupervised multi-prototype clustering for single-molecule electronics},
year = {2025},
journal = {Nano Research},
volume = {18},
number = {4},
pages = {94907276},
keywords = {single-molecule electronics, low-probability events detection, machine learning, artificial intelligence for science (AI4S)},
url = {https://www.sciopen.com/article/10.26599/NR.2025.94907276},
doi = {10.26599/NR.2025.94907276},
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.}
}