Nanotech Blogs and Articles

Molecular Hamiltonian Learning Extracts Parameters From Stm-Iets Data for Single Molecules

Researchers at Aalto University introduced "molecular Hamiltonian learning," a machine‑learning framework that infers the full Hamiltonian of single‑molecule magnets directly from set‑point‑dependent scanning tunneling spectroscopy (STM‑IETS) data. By training on a library of theoretical spectra that include crystal‑field, Coulomb and spin‑orbit coupling effects, the algorithm can quantitatively extract crystal‑field splitting, spin value, SOC strength, orbital energies and substrate‑induced mixing. The method was validated on FePc adsorbed on SnTe, successfully disentangling tip‑induced perturbations from intrinsic molecular properties. Source code and datasets have been released publicly, paving the way for automated, quantitative nanoscale characterisation.