Predictive Modeling of Critical Temperatures in Superconducting Materials.

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

SUBMITTER: Sizochenko N 

PROVIDER: S-EPMC7792800 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature