Machine-learning prediction of the formation of atomic gold wires by mechanically controlled break junctions.

One of the challenging issues in the formation of atomic wires in break-junction experiments is the formation of stable monoatomic chains of reasonable length. To address this issue, in this study, we present a combination of unsupervised and supervised machine learning models trained on the experimentally measured conductance traces, with a goal to develop a microscopic understanding. Applying a machine learning model to two independent data sets from two different samples containing 72 000 and 90 000 conductance-displacement traces of single-atomic junctions, respectively, we first obtain the optimum conditions of bias and the stretching rate for the formation of chains of length > 4 Å. A deep learning method is subsequently applied for the classification of individual breaking traces, leading to the identification of trace features related to long-chain formation. Further investigation by ab initio molecular dynamics simulations provides a molecular-level understanding of the problem.

Resonant transport in a highly conducting single molecular junction via metal-metal covalent bond.

Achieving highly transmitting molecular junctions through resonant transport at low bias is key to the next-generation low-power molecular devices. Although resonant transport in molecular junctions was observed by connecting a molecule between the metal electrodes via chemical anchors by applying a high source-drain bias (>1 V), the conductance was limited to <0.1G0, G0 being the quantum of conductance. Herein, we report electronic transport measurements by directly connecting a ferrocene molecule between Au electrodes under ambient conditions in a mechanically controllable break junction setup (MCBJ), revealing a conductance peak at ∼0.2G0 in the conductance histogram. A similar experiment was repeated for ferrocene terminated with amine (-NH2) and cyano (-CN) anchors, where conductance histograms exhibit an extended low conductance feature, including the sharp high conductance peak, similar to pristine ferrocene. The statistical analysis of the data and density functional theory-based transport calculation suggest a possible molecular conformation with a strong hybridization between the Au electrodes, and that the Fe atom of ferrocene is responsible for a near-perfect transmission in the vicinity of the Fermi energy, leading to the resonant transport at a small applied bias (<0.5 V). Moreover, calculations including van der Waals/dispersion corrections reveal a covalent-like organometallic bonding between Au and the central Fe atom of ferrocene, having bond energies of ∼660 meV. Overall, our study not only demonstrates the realization of an air-stable highly transmitting molecular junction, but also provides important insights about the nature of chemical bonding at the metal/organo-metallic interface.

Structural Regulation of Mechanical Gating in Molecular Junctions.

In contrast to silicon-based transistors, single-molecule junctions can be gated by simple mechanical means. Specifically, charge can be transferred between the junction's electrodes and its molecular bridge when the interelectrode distance is modified, leading to variations in the electronic transport properties of the junction. While this effect has been studied extensively, the influence of the molecular orientation on mechanical gating has not been addressed, despite its potential influence on the gating effectiveness. Here, we show that the same molecular junction can experience either clear mechanical gating or none, depending on the molecular orientation in the junctions. The effect is found in silver-ferrocene-silver break junctions and analyzed in view of ab initio and transport calculations, where the influence of the molecular orbital geometry on charge transfer to or from the molecule is revealed. The molecular orientation is thus a new degree of freedom that can be used to optimize mechanically gated molecular junctions.