Predicting the critical superconducting temperature using the random forest, MLP neural network, M5 model tree and multivariate linear regression

Using a random forest regression (RFR) machine learning technique, the critical temperature (Tc) of a superconductor was predicted in the context of Industry 4.0 in this study using features derived from the material's physico-chemical properties, containing atomic mass, electron affinity, atomic radius, valence, and thermal conductivity. The same experimental data were also fitted with multilayer perceptron (MLP) artificial neural networks (ANN), M5 model tree and multivariate linear regression (MLR) model for comparison. The current investigation's findings show that the proposed RFR–relied model can successfully forecast the critical temperature of a superconductor. Additionally, the Tc estimate was reached with a correlation coefficient of 0.9565 and a coefficient of determination 0.9146, when the observed dataset was used to test this unique technique. Additionally, the outcomes from the MLP, M5, and MLR models are obviously worse than those from the RFR–relied model. When it comes to fully comprehending the superconductivity, this investigation is noteworthy. Regarding forecasting effectiveness and feature reduction rate, the RFR approach has obvious advantages and generalizability, and it also demonstrates suitability for high-temperature superconductor Tc forecasting. In fact, it offers a practical and affordable approach to data-driven superconductor investigation.