Transformer-based deep learning structure–conductance relationships in gold and silver nanowires

Due to their inherently stochastic nature, microscopic configurations and conductance values of nano-junctions fabricated using break-junction techniques vary and fluctuate in and between experiments. Unfortunately, it is extremely difficult to observe the structural evolution of nano-junctions while measuring their conductance, a fact that prevents the establishment of their structure-conductance relationship. Herein, we conduct classical molecular dynamics (MD) simulations with neural-network potentials to simulate the stretching of Au and Ag nanowires followed by training a transformer-based neural network to predict their conductance. In addition to achieving an accuracy comparable to <i>ab initio</i> molecular dynamics within a computational cost similar to classical force fields, our approach can acquire the conductance of a large number of junction structures efficiently. Our calculations show that the transformer-based neural network, leveraging its self-attention mechanism, exhibits remarkable stability, accuracy and scalability in the prediction of zero-bias conductance of longer, larger and even structurally different gold nanowires when trained only on smaller systems. The simulated conductance histograms of gold nanowires are highly consistent with experiments. By examining the MD trajectories of gold nanowires simulated at 150 K and 300 K, we find that the formation probability of a three-strand planar structure appearing at 300 K is much higher than that at 150 K. This may be the dominating factor for the observed blueshift of the main peak positioned between 1.5-2<i>G</i>₀ in the conductance histogram following the temperature increase. Moreover, our transformer-based neural network pretrained on Au has an excellent transferability, which can be fine-tuned to predict accurately the conductance of Ag nanowires with much less training data. Our findings pave the way for using deep learning techniques in molecule-scale electronics and are helpful for elucidating the conducting mechanism of molecular junctions and improving their performance.