Generative Model Learning For Molecular Electronics

The use of single-molecule transistors in nanoelectronics devices requires a deep understanding of the generalized 'quantum impurity' models describing them. Microscopic models comprise molecular orbital complexity and strong electron interactions while also treating explicitly conduction electrons in the external circuit. No single theoretical method can treat the low-temperature physics of such systems exactly. To overcome this problem, we use a generative machine learning approach to formulate effective models that are simple enough to be treated exactly by methods such as the numerical renormalization group, but still capture all observables of interest of the physical system. We illustrate the power of the new methodology by application to the single benzene molecule transistor.