Machine learning protocol to identify pairing symmetries via quasiparticle interference imaging in Ising superconductors
This paper presents a machine-learning-guided protocol that integrates first-principles calculations and tight-binding modeling to accurately identify pairing symmetries in Ising superconductors, such as monolayer NbSe2, by analyzing quasiparticle interference data.