Investigation on Machine Learning Based Approaches for Estimating the Critical Temperature of Superconductors

TL;DR

This paper employs a stacking machine learning approach with hyperparameter optimization to accurately predict the critical temperature of superconductors, addressing a complex problem with promising performance metrics.

Contribution

It introduces a novel stacking ensemble method with hyperparameter tuning for estimating superconductors' critical temperatures, outperforming previous models.

Findings

Achieved RMSE of 9.68 in predictions

R2 score of 0.922 indicating high accuracy

Demonstrated the effectiveness of ensemble learning for this task

Abstract

Superconductors have been among the most fascinating substances, as the fundamental concept of superconductivity as well as the correlation of critical temperature and superconductive materials have been the focus of extensive investigation since their discovery. However, superconductors at normal temperatures have yet to be identified. Additionally, there are still many unknown factors and gaps of understanding regarding this unique phenomenon, particularly the connection between superconductivity and the fundamental criteria to estimate the critical temperature. To bridge the gap, numerous machine learning techniques have been established to estimate critical temperatures as it is extremely challenging to determine. Furthermore, the need for a sophisticated and feasible method for determining the temperature range that goes beyond the scope of the standard empirical formula appears to be strongly emphasized by various machine-learning approaches. This paper uses a stacking machine learning approach to train itself on the complex characteristics of superconductive materials in order to accurately predict critical temperatures. In comparison to other previous accessible research investigations, this model demonstrated a promising performance with an RMSE of 9.68 and an R2 score of 0.922. The findings presented here could be a viable technique to shed new insight on the efficient implementation of the stacking ensemble method with hyperparameter optimization (HPO).