Fast Data Sorting with Modified Principal Component Analysis to Distinguish Unique Single Molecular Break Junction Trajectories

Electric Field-Induced Sequential Prototropic Tautomerism in Enzyme-like Nanopocket Created by Single Molecular Break Junction

Mastering the control of external stimuli-induced chemical transformations with detailed insights into the mechanistic pathway is the key for developing efficient synthetic strategies and designing functional molecular systems. Enzymes, the most potent biological catalysts, efficiently utilize their built-in electric field to catalyze and control complex chemical reactions within the active site. Herein, we have demonstrated the interfacial electric field-induced prototropic tautomerization reaction in acylhydrazone entities by creating an enzymatic-like nanopocket within the atomically sharp gold electrodes using a mechanically controlled break junction (MCBJ) technique. In addition to that, the molecular system used here contains two coupled acylhydrazone reaction centers, hence demonstrating a cooperative stepwise electric field-induced reaction realized at the single molecular level. Furthermore, the mechanistic studies revealed a proton relay-assisted tautomerization showing the importance of external factors such as solvent in such electric field-driven reactions. Finally, single-molecule charge transport and energetics calculations of different molecular species at various applied electric fields using a polarizable continuum solvent model confirm and support our experimental findings. Thus, this study demonstrates that mimicking an enzymatic pocket using a single molecular junction's interfacial electric field as a trigger for chemical reactions can open new avenues to the field of synthetic chemistry.

From Molecular Electronics to Molecular Intelligence

Molecular electronics is a field that explores the ultimate limits of electronic device dimensions by using individual molecules as operable electronic devices. Over the past five decades since the proposal of a molecular rectifier by Aviram and Ratner in 1974 ( Chem. Phys. Lett.1974,29, 277-283), researchers have developed various fabrication and characterization techniques to explore the electrical properties of molecules. With the push of electrical characterizations and data analysis methodologies, the reproducibility issues of the single-molecule conductance measurement have been chiefly resolved, and the origins of conductance variation among different devices have been investigated. Numerous prototypical molecular electronic devices with external physical and chemical stimuli have been demonstrated based on the advances of instrumental and methodological developments. These devices enable functions such as switching, logic computing, and synaptic-like computing. However, as the goal of molecular electronics, how can molecular-based intelligence be achieved through single-molecule electronic devices? At the fiftieth anniversary of molecular electronics, we try to answer this question by summarizing recent progress and providing an outlook on single-molecule electronics. First, we review the fabrication methodologies for molecular junctions, which provide the foundation of molecular electronics. Second, the preliminary efforts of molecular logic devices toward integration circuits are discussed for future potential intelligent applications. Third, some molecular devices with sensing applications through physical and chemical stimuli are introduced, demonstrating phenomena at a single-molecule scale beyond conventional macroscopic devices. From this perspective, we summarize the current challenges and outlook prospects by describing the concepts of "AI for single-molecule electronics" and "single-molecule electronics for AI".

Making the Most of Nothing: One-Class Classification for Single-Molecule Transport Studies

Single-molecule experiments offer a unique means to probe molecular properties of individual molecules-yet they rest upon the successful control of background noise and irrelevant signals. In single-molecule transport studies, large amounts of data that probe a wide range of physical and chemical behaviors are often generated. However, due to the stochasticity of these experiments, a substantial fraction of the data may consist of blank traces where no molecular signal is evident. One-class (OC) classification is a machine learning technique to identify a specific class in a data set that potentially consists of a wide variety of classes. Here, we examine the utility of two different types of OC classification models on four diverse data sets from three different laboratories. Two of these data sets were measured at cryogenic temperatures and two at room temperature. By training the models solely on traces from a blank experiment, we demonstrate the efficacy of OC classification as a powerful and reliable method for filtering out blank traces from a molecular experiment in all four data sets. On a labeled 4,4'-bipyridine data set measured at 4.2 K, we achieve an accuracy of 96.9 ± 0.3 and an area under the receiver operating characteristic curve of 99.5 ± 0.3 as validated over a fivefold cross-validation. Given the wide range of physical and chemical properties that can be probed in single-molecule experiments, the successful application of OC classification to filter out blank traces is a major step forward in our ability to understand and manipulate molecular properties.

Methods for the analysis, interpretation, and prediction of single-molecule junction conductance behaviour

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.

Redox-controlled conductance of polyoxometalate molecular junctions

We demonstrate the reversible in situ photoreduction of molecular junctions of a phosphomolybdate [PMo₁₂O₄₀]^3- monolayer self-assembled on flat gold electrodes, connected by the tip of a conductive atomic force microscope. The conductance of the one electron reduced [PMo₁₂O₄₀]^4- molecular junction is increased by ∼10, and this open-shell state is stable in the junction in air at room temperature. The analysis of a large current-voltage dataset by unsupervised machine learning and clustering algorithms reveals that the electron transport in the pristine phosphomolybdate junctions leads to symmetric current-voltage curves, controlled by the lowest unoccupied molecular orbital (LUMO) at 0.6-0.7 eV above the Fermi energy with ∼25% of the junctions having a better electronic coupling to the electrodes than the main part of the dataset. This analysis also shows that a small fraction (∼18% of the dataset) of the molecules is already reduced. The UV light in situ photoreduced phosphomolybdate junctions systematically feature slightly asymmetric current-voltage behaviors, which is ascribed to the electron transport mediated by the single occupied molecular orbital (SOMO) nearly at resonance with the Fermi energy of the electrodes and by a closely located single unoccupied molecular orbital (SUMO) at ∼0.3 eV above the SOMO with a weak electronic coupling to the electrodes (∼50% of the dataset) or at ∼0.4 eV but with a better electrode coupling (∼50% of the dataset). These results shed light on the electronic properties of reversible switchable redox polyoxometalates, a key point for potential applications in nanoelectronic devices.

Trusting our machines: validating machine learning models for single-molecule transport experiments

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.

Single-Molecule Tunneling Sensors for Nitrobenzene Explosives

The tunneling current through the single-molecule junctions principally offers the ultimate solution for chemical and biochemical sensing via the interactions between probes and target analytes at the single-molecule level. However, it remains unexplored to achieve the sensitive and selective detection of targeted analytes using single-molecule junction techniques due to the challenge in quantitative evaluation of sensing sensitivity and selectivity. Herein, we demonstrate a single-molecule tunneling sensor for the highly sensitive and selective detection of nitrobenzene explosives using scanning tunneling microscope break junction (STM-BJ). Taking advantage of π-π stacking interactions between the molecular probes and nitrobenzene explosives, we use a spectral clustering algorithm to assign the signal of probes and π-stacked probes for sensitively detecting the targeted analytes and the distinguishable conductance change of probes when interacting with different nitroaromatic explosive compounds for selective detection. We find that pronounced conductance changes up to 0.8 orders of magnitude when the probes interact with TNT. Also, we obtain a sensitivity of up to ∼10 pM for TNT and high sensitivity for eight TNT analogues. Combined with theoretical calculations, we discover that the harness of the destructive quantum interference of the probe M1OH after interacting with TNT leads to high selectivity in sensing with TNT. Our work demonstrates the great potential of the single-molecule tunneling current for environmental sensing molecules with high selectivity and sensitivity.

Formation and Evolution of Metallocene Single-Molecule Circuits with Direct Gold-π Links

Single-molecule circuits with group 8 metallocenes are formed without additional linker groups in scanning tunneling microscope-based break junction (STMBJ) measurements at cryogenic and room-temperature conditions with gold (Au) electrodes. We investigate the nature of this direct gold-π binding motif and its effect on molecular conductance and persistence characteristics during junction evolution. The measurement technique under cryogenic conditions tracks molecular plateaus through the full cycle of extension and compression. Analysis reveals that junction persistence when the metal electrodes are pushed together correlates with whether electrodes are locally sharp or blunt, suggesting distinct scenarios for metallocene junction formation and evolution. The top and bottom surfaces of the "barrel"-shaped metallocenes present the electron-rich π system of cyclopentadienyl rings, which interacts with the gold electrodes in two distinct ways. An undercoordinated gold atom on a sharp tip forms a donor-acceptor bond to a specific carbon atom in the ring. However, a small, flat patch on a dull tip can bind more strongly to the ring as a whole through van der Waals interactions. Density functional theory (DFT)-based calculations of model electrode structures provide an atomic-scale picture of these scenarios, demonstrating the role of these bonding motifs during junction evolution and showing that the conductance is relatively independent of tip atomic-scale structure. The nonspecific interaction of the cyclopentadienyl rings with the electrodes enables extended conductance plateaus, a mechanism distinct from that identified for the more commonly studied, rod-shaped organic molecular wires.

Single-Molecule Junction Formation in Break-Junction Measurements

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.

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 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.

A machine learning approach for accurate and real-time DNA sequence identification

BACKGROUND

The all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results.

RESULTS

This article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair's identification accuracy to 99.5 %.

CONCLUSIONS

A monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands.

A review of oligo(arylene ethynylene) derivatives in molecular junctions

Oligo(arylene ethynylene) (OAE) derivatives are the "workhorse" molecules of molecular electronics. Their ease of synthesis and flexibility of functionalisation mean that a diverse array of OAE molecular wires have been designed, synthesised and studied theoretically and experimentally in molecular junctions using both single-molecule and ensemble methods. This review summarises the breadth of molecular designs that have been investigated with emphasis on structure-property relationships with respect to the electronic conductance of OAEs. The factors considered include molecular length, connectivity, conjugation, (anti)aromaticity, heteroatom effects and quantum interference (QI). Growing interest in the thermoelectric properties of OAE derivatives, which are expected to be at the forefront of research into organic thermoelectric devices, is also explored.

Deep learning-enabled prediction of 2D material breakdown

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.

Dissecting Time-Evolved Conductance Behavior of Single Molecule Junctions by Nonparametric Machine Learning

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.

Using Deep Learning to Identify Molecular Junction Characteristics

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.

Unsupervised feature recognition in single-molecule break junction data

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.

Automatic classification of single-molecule charge transport data with an unsupervised machine-learning algorithm

Single-molecule electrical characterization reveals the events occurring at the nanoscale, which provides guidelines for molecular materials and devices. However, data analysis to extract valuable information from the nanoscale measurement data remained as a major challenge. Herein, an unsupervised deep leaning method, a deep auto-encoder K-means (DAK) algorithm, is developed to distinguish different events from single-molecule charge transport measurements. As validated by three single-molecule junction systems, the method applies to the recognition for multiple compounds with various events and offers an effective data analysis method to track reaction kinetics at the single-molecule scale. This work opens the possibility of using deep unsupervised approaches to studying the physical and chemical processes at the single-molecule level.

Classification of conductance traces with recurrent neural networks

We present a new automated method for structural classification of the traces obtained in break junction experiments. Using recurrent neural networks trained on the traces of minimal cross-sectional area in molecular dynamics simulations, we successfully separate the traces into two classes: point contact or nanowire. This is done without any assumptions about the expected features of each class. The trained neural network is applied to experimental break junction conductance traces, and it separates the classes as well as the previously used experimental methods. The effect of using partial conductance traces is explored, and we show that the method performs equally well using full or partial traces (as long as the trace just prior to breaking is included). When only the initial part of the trace is included, the results are still better than random chance. Finally, we show that the neural network classification method can be used to classify experimental conductance traces without using simulated results for training, but instead training the network on a few representative experimental traces. This offers a tool to recognize some characteristic motifs of the traces, which can be hard to find by simple data selection algorithms.