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.

In situ impedance matching in Nb/Nb2O5/PtIr memristive nanojunctions for ultra-fast neuromorphic operation.

The dynamical aspects of bipolar resistive switchings have been investigated in Nb/Nb2O5/PtIr nanojunctions. We found that the widely tuneable ON and OFF state resistances are well separated at low bias. On the other hand, the high-bias regime of the resistive switchings coincides with the onset of a high nonlinearity in the current-voltage characteristics, where the impedance of both states rapidly decreases and becomes equivalent around 50 Ω. This phenomenon enables the overriding of the RC limitations of fast switchings between higher resistance ON and OFF states. Consequently, nanosecond switching times between multiple resistance states due to subnanosecond voltage pulses are demonstrated. Moreover, this finding provides the possibility of impedance engineering by the appropriate choice of voltage signals, which facilitates that both the set and reset transitions take place in an impedance matched manner to the surrounding circuit, demonstrating the merits of ultra-fast operation of Nb2O5 based neuromorphic networks.

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.

Electronic and mechanical characteristics of stacked dimer molecular junctions.

Break-junction measurements are typically aimed at characterizing electronic properties of single molecules bound between two metal electrodes. Although these measurements have provided structure-function relationships for such devices, there is little work that studies the impact of molecule-molecule interactions on junction characteristics. Here, we use a scanning tunneling microscope based break-junction technique to study pi-stacked dimer junctions formed with two amine-terminated conjugated molecules. We show that the conductance, force and flicker noise of such dimers differ dramatically when compared with the corresponding monomer junctions and discuss the implications of these results on intra- and inter-molecular charge transport.