Abstract
In this study, we have investigated quantitative relationships between critical temperatures of superconductive inorganic materials and the basic physicochemical attributes of these materials (also called quantitative structure-property relationships). We demonstrated that one of the most recent studies (titled "A data-driven statistical model for predicting the critical temperature of a superconductor” and published in Computational Materials Science by K. Hamidieh in 2018) reports on models that were based on the dataset that contains 27% of duplicate entries. We aimed to deliver stable models for a properly cleaned dataset using the same modeling techniques (multiple linear regression, MLR, and gradient boosting decision trees, XGBoost). The predictive ability of our best XGBoost model (R2 = 0.924, RMSE = 9.336 using 10-fold cross-validation) is comparable to the XGBoost model by the author of the initial dataset (R2 = 0.920 and RMSE = 9.5 K in ten-fold cross-validation). At the same time, our best model is based on less sophisticated parameters, which allows one to make more accurate interpretations while maintaining a generalizable model. In particular, we found that the highest relative influence is attributed to variables that represent the thermal conductivity of materials. In addition to MLR and XGBoost, we explored the potential of other machine learning techniques (NN, neural networks and RF, random forests).
Highlights
Superconducting materials are capable to conduct electric current with zero resistance at or below a certain critical temperature TC [1]
We found that 85% of materials had a single TC measurement reported (Figure 2a), and the remaining materials had at least 1 duplicate entry reported
We found that 85% of materials had a single TC measurement reported (Figure 2a), and the remaining materials had at least 1 duplicate entry reported (e.g., 1331 two values of TC reported)
Summary
Superconducting materials are capable to conduct electric current with zero resistance at or below a certain critical temperature TC [1]. Several theories analyze how superconductivity got established in materials. The commonly accepted Bardeen–Cooper–Schrieffer theory of superconductivity attributes the manifestation of superconductivity in a given material to the formation of resonant states of electron pairs [3,4,5]. It could be discussed in the context of the formation of ions that move through the crystalline lattice of the superconductor [6]. The phenomenon of superconductivity is widely applied in the industry: for example, superconductors are used to create powerful electromagnets, electrical systems, etc. Engineers generally follow empirical rules to create and test new superconducting materials
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