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

In single-molecule electronics, 4,4’-bipyridine is a well- characterized molecule that both serves as a fundamental testbed for developing experimental methodologies and showcases the complex behaviors expected from molecular electronic components. It is generally understood to exhibit a high- and a low-conductance state in break junction experiments which are attributed to a tilted and stretched configuration of a single molecule. 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 observe that the existing interpretation of a tilted and stretched configuration is inconsistent with our simulations. Instead, we propose that the existence of two molecules alters the geometry in the junction such that the conductance of the high-conductance state is more than 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 underscore the potential complexities 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.