Machine learning on the electron–boson mechanism in superconductors

Abstract

To unravel pairing mechanism of a superconductor from limited, indirect experimental data is always a difficult task. It is common but sometimes dubious to explain by a theoretical model with some tuning parameters. In this work, we propose that the machine learning might infer pairing mechanism from observables like superconducting gap functions. For superconductivity within the Migdal–Eliashberg theory, we perform supervised learning between superconducting gap functions and electron–boson spectral functions. For simple spectral functions, the neural network can easily capture the correspondence and predict perfectly. For complex spectral functions, an autoencoder is utilized to reduce the complexity of the spectral functions to be compatible to that of the gap functions. After this complexity-reduction process, relevant information of the spectral function is extracted and good performance restores. Our proposed method can extract relevant information from data and can be applied to general function-to-function mappings with asymmetric complexities either in physics or other fields.

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

“…One of the obstacles to such achievement is the calculation of (EBSF) and to address this, supervised and UML methods were applied. The results showed that the proposed methods could precisely predict EBSF with an accuracy above 99.9% [321]. Much research has been conducted to analyse the static properties of in-gap bound states for single and multiple quantum dots.…”

Upper limit of the transition temperature of superconducting materials

“… 25 , 26 , 27 ML models using different algorithms were trained to predict the existence of superconductivity and the T c of superconductors. 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 Progress has been made in several areas, such as how T c varies with doping, 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 the descriptors indicating superconducting mechanism, 36 , 37 , 38 , 39 structural factors affecting T c , 43 , 44 and candidates of new high- T c superconductors. 46 , 51 So far, ML models predicting T c have yielded good predictive scores.…”