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 ...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 highthroughput and high-quality data with clear physical mechanisms.Towards using artificial intelligence for single-molecule electronics(AI4SME),the unsupervised extraction of lowprobability events from the massive experimental data becomes the key step,which has emerged for accurate detection of different configurations and even structural changes in singlemolecule 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.展开更多
基金supported by the National Key Research and Development Program of China(No.2024YFA1208103)the National Natural Science Foundation of China(Nos.22403079,22173075,22325303,21933012,and 22250003)+1 种基金the Fujian Provincial Department of Science and Technology(Nos.2022H6014 and 2023H6002)the Fundamental Research Funds for the Central Universities(Nos.20720220020 and 20720200068).
文摘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 highthroughput and high-quality data with clear physical mechanisms.Towards using artificial intelligence for single-molecule electronics(AI4SME),the unsupervised extraction of lowprobability events from the massive experimental data becomes the key step,which has emerged for accurate detection of different configurations and even structural changes in singlemolecule 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.
