This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, we perform various data pre-processing steps to derive the clean DataG dataset, containing 13,022 compounds. In another stage of the study, we apply the novel CatBoost algorithm to predict the transition temperatures of novel superconducting materials. In addition, we developed a package called Jabir, which generates 322 atomic descriptors. We also designed an innovative hybrid method called the Soraya package to select the most critical features from the feature space. These yield R2 and RMSE values (0.952 and 6.45 K, respectively) superior to those previously reported in the literature. Finally, as a novel contribution to the field, a web application was designed for predicting and determining the Tc values of superconducting materials.
This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, we perform various data pre-processing steps to derive the clean DataG dataset, containing 13,022 compounds. In another stage of the study, we apply the novel CatBoost algorithm to predict the transition temperatures of novel superconducting materials. In addition, we developed a package called Jabir, which generates 322 atomic descriptors. We also designed an innovative hybrid method called the Soraya package to select the most critical features from the feature space. These yield R2 and RMSE values (0.952 and 6.45 K, respectively) superior to those previously reported in the literature. Finally, as a novel contribution to the field, a web application was designed for predicting and determining the Tc values of superconducting materials.
Superconductor databases
Literature review of databases with materials and . See literature review on ML models which utilize these datasets:
Notes on Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering
So far this is the most recent paper I've found on ML prediction of , improving on both modeling (CatBoost) and dataset compared to Stanev et al.
Critical temperature prediction models
Literature review of existing studies done on predicting with machine learning.
Superconductor databases
Literature review of databases with materials and . See literature review on ML models which utilize these datasets:
Notes on Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering
So far this is the most recent paper I've found on ML prediction of , improving on both modeling (CatBoost) and dataset compared to Stanev et al.
Critical temperature prediction models
Literature review of existing studies done on predicting with machine learning.