Understanding the Conductance Dispersion of Single-Molecule Junctions

Abstract

Molecular junctions have emerged as a powerful tool to investigate chemistry and physics at the single-molecule limit. However, their utility as a platform to develop spectroscopies and construct molecular devices is limited by the broad conductance dispersion typically encountered in experiments. The current view is that such broad dispersion arises because the detailed junction configuration is uncontrollable and varies in and between experiments. Contrary to conventional wisdom, through atomistic simulations and experiments, we show that even in ideal conditions, where the electrodes and electrode-molecule binding configurations are perfectly well-defined, measurements will still exhibit a broad conductance histogram. Such dispersion arises because of conductance changes as the junction is mechanically manipulated and the unavoidable stochastic nature of junction rupture. The results offer detailed atomistic insights into the factors that contribute to the broad conductance histograms and identify the key physical aspects that need to be controlled to narrow its width.

Learning Conductance: Gaussian Process Regression for Molecular Electronics

“…This aspect and its effects on the shape of the resulting conductance histograms has been studied by Li et al 47,48 . Importantly, the methods introduced below can also be used when time-averaged conductances are employed to construct histograms.…”

“…The shape of the conductance histograms cannot be obtained from calculations of a static junction in a minimum energy conformation, as these calculations do not take into account the conformational variability encountered in experiments. To capture the histograms, it is necessary to perform molecular dynamics (MD) simulations of junction formation and evolution using techniques such as classical force fields [42][43][44][45][46] , reactive force fields 47,48 , or ab-initio MD techniques [49][50][51][52][53][54] . Even for such simulations, complete sampling of the experimental situation remains challenging 55 .…”

“…Thus, to obtain meaningful histograms it is necessary to perform conductance calculations for several hundred to thousand snapshots and for relatively large systems, since parts of the gold electrodes have to be included in the calculations in order to correctly describe their interactions with the molecules under study. This comes with a significant computational cost, so, for simulating histograms, one usually has to rely on cheaper and simpler methods of limited accuracy to obtain information about the electron transport, such as (extended) Hückel-based calculations 48,59 .…”

“…While we chose reactive force fields 94,95 and density functional tight-binding (DFTB) 96 calculations for our simulations, any method which performs the structural sampling of a molecular junction and provides transport properties for selected snapshots may provide the basis for our machine learning approach. MD simulations employing reactive force fields have been successfully used in the past to compute trajectories of MCBJ experiments for the same molecule used here or similar systems 47,48,97 . Since electronic structure calculations are the most expensive component in the construction of the conductance histograms, we aim to predict conductance solely on structural information.…”

Long-Range Charge Transport in Homogeneous and Alternating-Rigidity Chains

“…[12][13][14][15] , in long junctions such methods reach a) Electronic mail: francisco.lai@mail.utoronto.ca b) Electronic mail: dvira.segal@utoronto.ca their computational limit. Alternatively, a common approximate approach to deal with the impact of nuclear effects on electronic conduction is to employ classical molecular dynamics simulations for sampling molecular configurations, in conjunction with the Green's function formalism for transport [16][17][18][19][20][21][22] . Yet, even this approximate method becomes computationally impractical for large systems and it requires an additional level of coarsegraining 17 .…”