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Gerry M, Wang JJ, Li J, Shein-Lumbroso O, Tal O, Segal D. Machine learning delta-T noise for temperature bias estimation. J Chem Phys 2025; 162:084108. [PMID: 40008946 DOI: 10.1063/5.0250879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2024] [Accepted: 01/30/2025] [Indexed: 02/27/2025] Open
Abstract
Delta-T shot noise is activated in temperature-biased electronic junctions, down to the atomic scale. It is characterized by a quadratic dependence on the temperature difference and a nonlinear relationship with the transmission coefficients of partially opened conduction channels. In this work, we demonstrate that delta-T noise, measured across an ensemble of atomic-scale junctions, can be utilized to estimate the temperature bias in these systems. Our approach employs a supervised machine learning algorithm to train a neural network, with input features being the scaled electrical conductance, the delta-T noise, and the mean temperature. Due to limited experimental data, we generate synthetic datasets, designed to mimic experiments. The neural network, trained on these synthetic data, was subsequently applied to predict temperature biases from experimental datasets. Using performance metrics, we demonstrate that the mean bias-the deviation of predicted temperature differences from their true value-is less than 1 K for junctions with conductance up to 4G0. Our study highlights that, while a single delta-T noise measurement is insufficient for accurately estimating the applied temperature bias due to noise contributions from other sources, averaging over an ensemble of junctions enables predictions within experimental uncertainties. This suggests that machine learning approaches can be utilized for estimation of temperature biases and similarly other stimuli in electronic junctions.
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Affiliation(s)
- Matthew Gerry
- Department of Physics, University of Toronto, 60 Saint George St., Toronto, Ontario M5S 1A7, Canada
| | - Jonathan J Wang
- Department of Chemistry, University of Toronto, 80 Saint George St., Toronto, Ontario M5S 3H6, Canada
| | - Joanna Li
- Department of Physics, University of Toronto, 60 Saint George St., Toronto, Ontario M5S 1A7, Canada
- Division of Engineering Science, University of Toronto, 42 Saint George St., Toronto, Ontario M5S 2E4, Canada
| | - Ofir Shein-Lumbroso
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Oren Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Dvira Segal
- Department of Physics, University of Toronto, 60 Saint George St., Toronto, Ontario M5S 1A7, Canada
- Department of Chemistry, University of Toronto, 80 Saint George St., Toronto, Ontario M5S 3H6, Canada
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2
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Gorenskaia E, Low PJ. Methods for the analysis, interpretation, and prediction of single-molecule junction conductance behaviour. Chem Sci 2024; 15:9510-9556. [PMID: 38939131 PMCID: PMC11206205 DOI: 10.1039/d4sc00488d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 05/06/2024] [Indexed: 06/29/2024] Open
Abstract
This article offers a broad overview of measurement methods in the field of molecular electronics, with a particular focus on the most common single-molecule junction fabrication techniques, the challenges in data analysis and interpretation of single-molecule junction current-distance traces, and a summary of simulations and predictive models aimed at establishing robust structure-property relationships of use in the further development of molecular electronics.
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Affiliation(s)
- Elena Gorenskaia
- School of Molecular Sciences, University of Western Australia 35 Stirling Highway Crawley Western Australia 6026 Australia
| | - Paul J Low
- School of Molecular Sciences, University of Western Australia 35 Stirling Highway Crawley Western Australia 6026 Australia
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3
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Komoto Y, Ryu J, Taniguchi M. Total variation denoising-based method of identifying the states of single molecules in break junction data. DISCOVER NANO 2024; 19:20. [PMID: 38285285 PMCID: PMC10825082 DOI: 10.1186/s11671-024-03963-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/22/2024] [Indexed: 01/30/2024]
Abstract
Break junction (BJ) measurements provide insights into the electrical properties of diverse molecules, enabling the direct assessment of single-molecule conductances. The BJ method displays potential for use in determining the dynamics of individual molecules, single-molecule chemical reactions, and biomolecules, such as deoxyribonucleic acid and ribonucleic acid. However, conductance data obtained via single-molecule measurements may be susceptible to fluctuations due to minute structural changes within the junctions. Consequently, clearly identifying the conduction states of these molecules is challenging. This study aims to develop a method of precisely identifying conduction state traces. We propose a novel single-molecule analysis approach that employs total variation denoising (TVD) in signal processing, focusing on the integration of information technology with measured single-molecule data. We successfully applied this method to simulated conductance traces, effectively denoise the data, and elucidate multiple conduction states. The proposed method facilitates the identification of well-defined plateau lengths and supervised machine learning with enhanced accuracies. The introduced TVD-based analytical method is effective in elucidating the states within the measured single-molecule data. This approach exhibits the potential to offer novel perspectives regarding the formation of molecular junctions, conformational changes, and cleavage.
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Affiliation(s)
- Yuki Komoto
- SANKEN, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan.
- Artificial Intelligence Research Center, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan.
| | - Jiho Ryu
- SANKEN, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
| | - Masateru Taniguchi
- SANKEN, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
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4
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Tao S, Zhang Q, Pitie S, Liu C, Fan Y, Zhao C, Seydou M, Dappe YJ, Nichols RJ, Yang L. Revealing conductance variation of molecular junctions based on an unsupervised data analysis approach. Electrochim Acta 2023. [DOI: 10.1016/j.electacta.2023.142225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
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5
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Shijie Gao, Liu X, Liu X, Chen D, Guo H, Yin J. Predicting the AC Conductivity of Nanocomposite Films using the Bagging Model. POLYMER SCIENCE SERIES A 2022. [DOI: 10.1134/s0965545x22700559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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6
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Bro-Jørgensen W, Hamill JM, Bro R, Solomon GC. Trusting our machines: validating machine learning models for single-molecule transport experiments. Chem Soc Rev 2022; 51:6875-6892. [PMID: 35686581 PMCID: PMC9377421 DOI: 10.1039/d1cs00884f] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Indexed: 11/21/2022]
Abstract
In this tutorial review, we will describe crucial aspects related to the application of machine learning to help users avoid the most common pitfalls. The examples we present will be based on data from the field of molecular electronics, specifically single-molecule electron transport experiments, but the concepts and problems we explore will be sufficiently general for application in other fields with similar data. In the first part of the tutorial review, we will introduce the field of single-molecule transport, and provide an overview of the most common machine learning algorithms employed. In the second part of the tutorial review, we will show, through examples grounded in single-molecule transport, that the promises of machine learning can only be fulfilled by careful application. We will end the tutorial review with a discussion of where we, as a field, could go from here.
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Affiliation(s)
- William Bro-Jørgensen
- Department of Chemistry and Nano-Science Center, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen Ø, Denmark.
| | - Joseph M Hamill
- Department of Chemistry and Nano-Science Center, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen Ø, Denmark.
| | - Rasmus Bro
- Department of Food Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark.
| | - Gemma C Solomon
- Department of Chemistry and Nano-Science Center, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen Ø, Denmark.
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7
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Fu T, Frommer K, Nuckolls C, Venkataraman L. Single-Molecule Junction Formation in Break-Junction Measurements. J Phys Chem Lett 2021; 12:10802-10807. [PMID: 34723548 DOI: 10.1021/acs.jpclett.1c03160] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The scanning tunneling microscope-based break-junction (STM-BJ) technique is the most common method used to study the electronic properties of single-molecule junctions. It relies on repeatedly forming and rupturing a Au contact in an environment of the target molecules. The probability of junction formation is typically very high (∼70-95%), prompting questions relating to how the nanoscale structure of the Au electrode before the metal point contact ruptures alters junction formation. Here we analyze conductance traces measured with the STM-BJ setup by combining correlation analysis and multiple machine learning tools, including gradient-boosted trees and neural networks. We show that two key features describing the Au-Au contact prior to rupture determine the extent of contact relaxation (snapback) and the probability of junction formation. Importantly, our data strongly indicate that molecular junctions are formed prior to the rupture of the Au-Au contact, explaining the high probability of junction formation observed in room-temperature solution measurements.
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Affiliation(s)
- Tianren Fu
- Department of Chemistry, Columbia University, New York, New York 10027, United States
| | - Kathleen Frommer
- Department of Chemistry, Columbia University, New York, New York 10027, United States
| | - Colin Nuckolls
- Department of Chemistry, Columbia University, New York, New York 10027, United States
| | - Latha Venkataraman
- Department of Chemistry, Columbia University, New York, New York 10027, United States
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, United States
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Lin D, Zhao Z, Pan H, Li S, Wang Y, Wang D, Sanvito S, Hou S. Using Weakly Supervised Deep Learning to Classify and Segment Single-Molecule Break-Junction Conductance Traces. Chemphyschem 2021; 22:2107-2114. [PMID: 34324254 DOI: 10.1002/cphc.202100414] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/23/2021] [Indexed: 11/11/2022]
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.
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Affiliation(s)
- Dongying Lin
- Department of Electronics, Peking University, Center for Nanoscale Science and Technology, Key Laboratory for the Physics and Chemistry of Nanodevices, Beijing, 100871, China
| | - Zhihao Zhao
- 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
| | - Haoyang Pan
- Department of Electronics, Peking University, Center for Nanoscale Science and Technology, Key Laboratory for the Physics and Chemistry of Nanodevices, Beijing, 100871, China
| | - Shi Li
- Department of Electronics, Peking University, Center for Nanoscale Science and Technology, Key Laboratory for the Physics and Chemistry of Nanodevices, Beijing, 100871, China
| | - Yongfeng Wang
- Department of Electronics, Peking University, Center for Nanoscale Science and Technology, Key Laboratory for the Physics and Chemistry of Nanodevices, Beijing, 100871, China
| | - Dong Wang
- 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
| | - Stefano Sanvito
- School of Physics, Trinity College, AMBER and CRANN Institute, Dublin 2, Ireland
| | - Shimin Hou
- Department of Electronics, Peking University, Center for Nanoscale Science and Technology, Key Laboratory for the Physics and Chemistry of Nanodevices, Beijing, 100871, China
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9
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Huan YQ, Liu Y, Goh KEJ, Wong SL, Lau CS. Deep learning-enabled prediction of 2D material breakdown. NANOTECHNOLOGY 2021; 32:265203. [PMID: 33361556 DOI: 10.1088/1361-6528/abd655] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/23/2020] [Indexed: 06/12/2023]
Abstract
Characterizing electrical breakdown limits of materials is a crucial step in device development. However, methods for repeatable measurements are scarce in two-dimensional materials, where breakdown studies have been limited to destructive methods. This restricts our ability to fully account for variability in local electronic properties induced by surface contaminants and the fabrication process. To tackle this, we implement a two-step deep-learning model to predict the breakdown mechanism and breakdown voltage of monolayer MoS2devices with varying channel lengths and resistances using current measured in the low-voltage regime as inputs. A deep neural network (DNN) first classifies between Joule and avalanche breakdown mechanisms using partial current traces from 0 to 20 V. Following this, a convolutional long short-term memory network (CLSTM) predicts breakdown voltages of these classified devices based on partial current traces. We test our model with electrical measurements collected using feedback-control of the applied voltage to prevent device destruction, and show that the DNN classifier achieves an accuracy of 79% while the CLSTM model has a 12% error when requiring only 80% of the current trace as inputs. Our results indicate that information encoded in the current behavior far from the breakdown point can be used for breakdown predictions, which will enable non-destructive and rapid material characterization for 2D material device development.
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Affiliation(s)
- Yan Qi Huan
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, 08-03 Innovis, 138634, Singapore
| | - Yincheng Liu
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, 08-03 Innovis, 138634, Singapore
| | - Kuan Eng Johnson Goh
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, 08-03 Innovis, 138634, Singapore
- Department of Physics, National University of Singapore, 2 Science Drive 3, 117551, Singapore
| | - Swee Liang Wong
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, 08-03 Innovis, 138634, Singapore
- Department of Physics, National University of Singapore, 2 Science Drive 3, 117551, Singapore
| | - Chit Siong Lau
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research, 2 Fusionopolis Way, 08-03 Innovis, 138634, Singapore
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10
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Bag S, Aggarwal A, Maiti PK. Machine Learning Prediction of Electronic Coupling between the Guanine Bases of DNA. J Phys Chem A 2020; 124:7658-7664. [DOI: 10.1021/acs.jpca.0c04368] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Saientan Bag
- Center for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India
| | - Abhishek Aggarwal
- Center for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India
| | - Prabal K. Maiti
- Center for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India
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11
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Vladyka A, Albrecht T. Unsupervised classification of single-molecule data with autoencoders and transfer learning. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/aba6f2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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12
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Liu B, Murayama S, Komoto Y, Tsutsui M, Taniguchi M. Dissecting Time-Evolved Conductance Behavior of Single Molecule Junctions by Nonparametric Machine Learning. J Phys Chem Lett 2020; 11:6567-6572. [PMID: 32668163 DOI: 10.1021/acs.jpclett.0c01948] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Improved understanding of charge transport in single molecules is essential for utilizing their potential as circuit components at the nanosize limit. However, reliable analyses of varying tunneling current acquired by break junction experiments remain an ongoing challenge to find molecular feature structure-property relationships. In this work, we report on an unsupervised learning approach for investigating molecular signatures in conductance traces. Our hybrid machine learning algorithm compares grids of data in conductance-time domains and judges the similarity without any researcher-crafted parameters to identify fine molecular components that may otherwise be obscured by background fluctuations. We demonstrate its ability for classifying Au-alkanedithiol-Au conductance traces acquired with microfabricated mechanically controllable break junctions. The unbiased procedure was able to not only judge the presence or absence of the carbon chains in the electrode gap but also to identify multiple conductance states of the molecular tunneling junctions with different conformations. This finding may offer a useful tool for studying single-molecule properties using break junction methods.
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Affiliation(s)
- Bo Liu
- The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
| | - Sanae Murayama
- The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
| | - Yuki Komoto
- The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
| | - Makusu Tsutsui
- The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
| | - Masateru Taniguchi
- The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
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Fu T, Zang Y, Zou Q, Nuckolls C, Venkataraman L. Using Deep Learning to Identify Molecular Junction Characteristics. NANO LETTERS 2020; 20:3320-3325. [PMID: 32242671 DOI: 10.1021/acs.nanolett.0c00198] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
The scanning tunneling microscope-based break junction (STM-BJ) is used widely to create and characterize single metal-molecule-metal junctions. In this technique, conductance is continuously recorded as a metal point contact is broken in a solution of molecules. Conductance plateaus are seen when stable molecular junctions are formed. Typically, thousands of junctions are created and measured, yielding thousands of distinct conductance versus extension traces. However, such traces are rarely analyzed individually to recognize the types of junctions formed. Here, we present a deep learning-based method to identify molecular junctions and show that it performs better than several commonly used and recently reported techniques. We demonstrate molecular junction identification from mixed solution measurements with accuracies as high as 97%. We also apply this model to an in situ electric field-driven isomerization reaction of a [3]cumulene to follow the reaction over time. Furthermore, we demonstrate that our model can remain accurate even when a key parameter, the average junction conductance, is eliminated from the analysis, showing that our model goes beyond conventional analysis in existing methods.
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Affiliation(s)
- Tianren Fu
- Department of Chemistry, Columbia University, New York, New York 10027, United States
| | - Yaping Zang
- Department of Chemistry, Columbia University, New York, New York 10027, United States
| | - Qi Zou
- Department of Chemistry, Columbia University, New York, New York 10027, United States
- Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai 200090, China
| | - Colin Nuckolls
- Department of Chemistry, Columbia University, New York, New York 10027, United States
| | - Latha Venkataraman
- Department of Chemistry, Columbia University, New York, New York 10027, United States
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, United States
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Magyarkuti A, Balogh N, Balogh Z, Venkataraman L, Halbritter A. Unsupervised feature recognition in single-molecule break junction data. NANOSCALE 2020; 12:8355-8363. [PMID: 32239021 DOI: 10.1039/d0nr00467g] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Single-molecule break junction measurements deliver a huge number of conductance vs. electrode separation traces. During such measurements, the target molecules may bind to the electrodes in different geometries, and the evolution and rupture of the single-molecule junction may also follow distinct trajectories. The unraveling of the various typical trace classes is a prerequisite to the proper physical interpretation of the data. Here we exploit the efficient feature recognition properties of neural networks to automatically find the relevant trace classes. To eliminate the need for manually labeled training data we apply a combined method, which automatically selects training traces according to the extreme values of principal component projections or some auxiliary measured quantities. Then the network captures the features of these characteristic traces and generalizes its inference to the entire dataset. The use of a simple neural network structure also enables a direct insight into the decision-making mechanism. We demonstrate that this combined machine learning method is efficient in the unsupervised recognition of unobvious, but highly relevant trace classes within low and room temperature gold-4,4' bipyridine-gold single-molecule break junction data.
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Affiliation(s)
- András Magyarkuti
- Department of Physics, Budapest University of Technology and Economics, 1111 Budapest, Budafoki ut 8, Hungary.
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