A Machine Learning Based Investigative Analysis for Predicting the Critical Temperature of Superconductors

INTRODUCTION: Ever since the initial discovery of superconductivity, the fundamental concept and the complex relationship between critical temperature and superconductive materials have been subject to extensive investigation. However, identifying superconductors that exhibit such behavior at normal temperatures remains a significant challenge, and there are still significant gaps in our understanding of this unique phenomenon, particularly regarding the fundamental criteria used to estimate critical temperature. OBJECTIVES: To address this knowledge gap, a plethora of machine learning techniques have been developed to model critical temperatures, given the inherent difficulty in predicting them using traditional methods. METHODS: Additionally, the limitations of the standard empirical formula in determining the temperature range require the development of more advanced and viable methods. This article presents an investigative analysis on the performance achieved by different supervised machine learning algorithms when used with three different feature selection techniques. RESULTS: The stacking model used in this work is found to be the best performer among all the algorithms tested, as reflected by the Root Mean Squared Error (RMSE) of 9.68, R2 score of 0.922, Mean Absolute Error (MAE) score of 5.383, and Mean Absolute Percentage Error (MAPE) score of 4.575. CONCLUSION: Therefore, it is observed that ML algorithms can contribute significantly in the domain of predictive analysis of modeling critical temperatures in superconductors and can assist in developing a robust computer-aided system to aid the education personals and research scientists to further assess the performance of the ML models.