High-Tc superconductor candidates proposed by machine learning
Bibliographic record
Abstract
The following are contained: Python code to generate features to input into machine learning models for superconducting critical temperatures, as well as the code to implement the machine learning models. Chemical compositions, critical temperatues, and pressures at which the critcial temperatures were measured ("0" indicates ambient pressure, "1" indicates applied pressure) of materials in our cleaned SuperCon data set. Critical temeprature predictions and weight coefficients for SuperCon materials at implicit pressure and ambient pressure (made only for those samples with pressures of "0") Chemical compositions, identifiers, energies above convex hulls, band gaps, and machine learning features for samples in Materials Project. Critical temperature predictions and weight coefficients for high-Tc candidate samples in Materials Project under ambient pressure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.000 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.036 |
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; both teacher heads agree on what is shown here.
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".