Predictive Modeling of Critical Temperatures in Superconducting Materials
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 stu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/26/1/8 |
| _version_ | 1797543851049615360 |
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| author | Natalia Sizochenko Markus Hofmann |
| author_facet | Natalia Sizochenko Markus Hofmann |
| author_sort | Natalia Sizochenko |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-10T13:52:18Z |
| format | Article |
| id | doaj.art-6dd20b1bf07d46ea807bf04f0dfb0de0 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T13:52:18Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-6dd20b1bf07d46ea807bf04f0dfb0de02023-11-21T02:02:35ZengMDPI AGMolecules1420-30492020-12-01261810.3390/molecules26010008Predictive Modeling of Critical Temperatures in Superconducting MaterialsNatalia Sizochenko0Markus Hofmann1Department of Informatics, Blanchardstown Campus, Technological University Dublin, 15 YV78 Dublin, IrelandDepartment of Informatics, Blanchardstown Campus, Technological University Dublin, 15 YV78 Dublin, IrelandIn 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).https://www.mdpi.com/1420-3049/26/1/8critical temperaturethermal conductivitypredictive modelingQSPRmachine learning |
| spellingShingle | Natalia Sizochenko Markus Hofmann Predictive Modeling of Critical Temperatures in Superconducting Materials Molecules critical temperature thermal conductivity predictive modeling QSPR machine learning |
| title | Predictive Modeling of Critical Temperatures in Superconducting Materials |
| title_full | Predictive Modeling of Critical Temperatures in Superconducting Materials |
| title_fullStr | Predictive Modeling of Critical Temperatures in Superconducting Materials |
| title_full_unstemmed | Predictive Modeling of Critical Temperatures in Superconducting Materials |
| title_short | Predictive Modeling of Critical Temperatures in Superconducting Materials |
| title_sort | predictive modeling of critical temperatures in superconducting materials |
| topic | critical temperature thermal conductivity predictive modeling QSPR machine learning |
| url | https://www.mdpi.com/1420-3049/26/1/8 |
| work_keys_str_mv | AT nataliasizochenko predictivemodelingofcriticaltemperaturesinsuperconductingmaterials AT markushofmann predictivemodelingofcriticaltemperaturesinsuperconductingmaterials |