Automated derivation of effective models for quantum impurity models

The theoretical description of quantum transport through nanodevices such as molecular junctions necessitates the formulation of models that are sufficiently complex to capture strong electronic interactions and orbital/chemical structure, but simple enough to be treated with methods like the Numerical Renormalisation Group (NRG). The sheer diversity of different molecules requires extremely flexible effective models, capable of faithfully representing the properties of such systems. To overcome these difficulties we introduce a Machine Learning algorithm that combines convex and non-convex optimization to automatically derive a symmetry-inspired effective impurity model, that is simple enough to be simulated using NRG. The resulting model can be used to carry out highly accurate conductance calculations, which we benchmark against brute-force calculations for the simplest systems. The methodology will allow an accurate treatment of larger strongly correlated systems, beyond present capabilities.