A data-driven statistical model for predicting the critical temperature of a superconductor
We estimate a statistical model to predict the superconducting critical temperature based on the features extracted from the superconductor’s chemical formula. The statistical model gives reasonable out-of-sample predictions: ±9.5 K based on root-mean-squared-error. Features extracted based on therm...
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| Published in | Computational materials science Vol. 154; pp. 346 - 354 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.11.2018
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| Subjects | |
| Online Access | Get full text |
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| Abstract | We estimate a statistical model to predict the superconducting critical temperature based on the features extracted from the superconductor’s chemical formula. The statistical model gives reasonable out-of-sample predictions: ±9.5 K based on root-mean-squared-error. Features extracted based on thermal conductivity, atomic radius, valence, electron affinity, and atomic mass contribute the most to the model’s predictive accuracy. It is crucial to note that our model does not predict whether a material is a superconductor or not; it only gives predictions for superconductors. |
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| AbstractList | We estimate a statistical model to predict the superconducting critical temperature based on the features extracted from the superconductor’s chemical formula. The statistical model gives reasonable out-of-sample predictions: ±9.5 K based on root-mean-squared-error. Features extracted based on thermal conductivity, atomic radius, valence, electron affinity, and atomic mass contribute the most to the model’s predictive accuracy. It is crucial to note that our model does not predict whether a material is a superconductor or not; it only gives predictions for superconductors. |
| Author | Hamidieh, Kam |
| Author_xml | – sequence: 1 givenname: Kam orcidid: 0000-0003-3606-2775 surname: Hamidieh fullname: Hamidieh, Kam email: hkam@wharton.upenn.edu organization: Statistics Department, The Wharton School, University of Pennsylvania, 400 Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA 19104-6340, United States |
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| Cites_doi | 10.1021/acs.jcim.6b00591 10.1006/inco.1995.1136 10.1109/39.841342 10.1103/PhysRev.97.74 10.1088/0953-2048/29/8/080502 10.1007/BF00116037 10.7566/JPSJ.86.123701 10.1007/s10948-014-2891-7 10.1214/aos/1013203451 10.1145/2939672.2939785 |
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| SubjectTerms | Critical temperature Data mining Machine learning Statistical learning Superconductivity Superconductor |
| Title | A data-driven statistical model for predicting the critical temperature of a superconductor |
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