Learning pairing symmetries in disordered superconductors using spin-polarized local density of states

Abstract

We construct an artificial neural network to study the pairing symmetries in disordered superconductors. For Hamiltonians on square lattice with s-wave, d-wave, and nematic pairing potentials, we use the spin-polarized local density of states near a magnetic impurity in the clean system to train the neural network. We find that, when the depth of the artificial neural network is sufficient large, it will have the power to predict the pairing symmetries in disordered superconductors. In a large parameter regime of the potential disorder, the artificial neural network predicts the correct pairing symmetries with relatively high confidences.

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

“…Properties for various pairing symmetries of spinpolarised local density of states can be extracted using DL methods. To do this, a different Hamiltonian model should be built first' after that a fitting on experimental data or the first principal calculation could present the parameters of such a model, and, at last, the data can be fed to deep NNs for the purpose of training [324]. The ability to create multicentre bonds in allotropes and borides, and an electron deficiency of boron, makes this element interesting for SCs.…”