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.

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

The dynamical aspects of bipolar resistive switchings have been investigated in Nb/NbO/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.

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.

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.