Prediction of superconducting properties of materials based on machine learning models

TL;DR

This paper employs advanced machine learning models to predict key properties of superconducting materials, significantly accelerating the discovery process and identifying promising candidates with high critical temperatures.

Contribution

It introduces novel applications of XGBoost and deep forest models to predict superconducting properties, achieving state-of-the-art results and enabling efficient material screening.

Findings

Identified 50 candidate superconductors with potential Tc > 90 K

First application of deep forest to predict superconducting critical temperature

Achieved state-of-the-art accuracy in property prediction

Abstract

The application of superconducting materials is becoming more and more widespread. Traditionally, the discovery of new superconducting materials relies on the experience of experts and a large number of "trial and error" experiments, which not only increases the cost of experiments but also prolongs the period of discovering new superconducting materials. In recent years, machine learning has been increasingly applied to materials science. Based on this, this manuscript proposes the use of XGBoost model to identify superconductors; the first application of deep forest model to predict the critical temperature of superconductors; the first application of deep forest to predict the band gap of materials; and application of a new sub-network model to predict the Fermi energy level of materials. Compared with our known similar literature, all the above algorithms reach state-of-the-art. Finally, this manuscript uses the above models to search the COD public dataset and identify 50 candidate superconducting materials with possible critical temperature greater than 90 K.