Abstract
Measuring the critical temperature of a superconductor experimentally is a very arduous process as critical temperatures of superconductors mostly lie on the extreme lower end on the Kelvin scale. Estimating them with the help of high-end instruments and lab experiments incurs high costs. In this paper, we employ and compare the performance of several regression algorithms which predict the critical temperature of superconductors based on the physical and chemical properties of the materials that constitute them, which are derived from the chemical formula of the superconductor. We compare state-of-the-art algorithms based on their accuracy of prediction using metrics like MAE, MSE, RMSE and R2 score. Random forest and XGBoost provide best results in this task of predicting superconducting critical temperature.
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Kulkarni, A.K., Puranik, V., Kulkarni, R. (2022). Empirical Study of Predicting Critical Temperature of a Superconductor Using Regression Techniques. In: Gandhi, T.K., Konar, D., Sen, B., Sharma, K. (eds) Advanced Computational Paradigms and Hybrid Intelligent Computing . Advances in Intelligent Systems and Computing, vol 1373. Springer, Singapore. https://doi.org/10.1007/978-981-16-4369-9_39
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DOI: https://doi.org/10.1007/978-981-16-4369-9_39
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