Material informatics for layered high-<i>T</i> <i>C</i> superconductors
Bibliographic record
Abstract
Superconductors were typically discovered by trial-and-error aided by the knowledge and intuition of individual researchers. In this work, using materials informatics aided by machine learning (ML), we build an ML model of superconductors, which is based on several material descriptors with apparent physical meanings to efficiently predict critical superconducting temperature TC. The descriptors include the average atomic mass of a compound, the average number of electrons in an unfilled shell, the average ground state atomic magnetic moments, the maximum difference of electronegativity, etc. To fully optimize the ML model, we develop a multi-step learning and multi-algorithm cross-verification approach. For known high TC superconductors, our ML model predicts excellent TC values with over 92% confidence. When the ML model is applied to about 2500 layered materials in the inorganic crystal structure database, 25 of them are predicted to be superconductors not known before, including 12 cuprates, 7 iron-based crystals, and 6 others, with TC ranging from ∼32 K to ∼138 K. The findings shed considerable light on the mapping between the material descriptors and TC for layered superconductors. The ML calculates that in our descriptors, the maximum difference of electronegativity is the most important one.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".