Machine Learning-Enhanced Molecular Dynamics of 4,4′-Bipyridine in a Break Junction: A Supramolecular Origin of the High Conductance

Abstract In single-molecule electronics, 4,4′-bipyridine is a well-characterized molecule that serves both as a fundamental testbed for developing experimental methodologies and showcases the complex behaviors expected from molecular electronic components. In break junction experiments, it is generally understood to exhibit a high- and a low-conductance state that are attributed to a tilted and stretched single-molecule configuration. Despite this established view, we suggest a supramolecular origin for these conductance states that has not previously been considered. Our findings indicate that the high- and low-conductance states may arise from the presence of two molecules and one molecule, respectively, challenging the conventional interpretation. Using a state-of-the-art machine learning force field called the neuroevolution potential, we find that the conventional interpretation of a tilted and stretched configuration is inconsistent with our simulations. Instead, our results suggest that the presence of two molecules promotes junction geometries and gold rearrangements that stabilize a high-conductance state whose transmission exceeds twice that of the low-conductance state. Furthermore, we compare this interpretation to the existing literature of diverse experimental data on 4,4′-bipyridine in break junctions and find that it is consistent with the broader body of experimental evidence. Our results highlight the structural complexity of molecular behavior within break junctions, suggesting even more complex dynamics than originally anticipated. In this light, a broader examination of the dynamics of molecules in single-molecule break junctions with machine learning-assisted molecular dynamics is warranted.