Predictive Modeling in High-Temperature Superconductors: Comparative Insights from Density Functional Theory and Machine Learning
Bibliographic record
Abstract
Superconductivity, defined by the disappearance of electrical resistance below a critical temperature, holds significant promise for future technologies such as quantum computing, maglev transport, and energy-efficient power grids. Among the many superconducting materials, nickel and copper oxide stand out due to their comparable layered structures but distinct electronic properties. Understanding and predicting their superconducting behavior is essential for discovering new high-temperature superconductors. Density functional theory and machine learning have become indispensable tools in this effort. While DFT offers insights into band structure and orbital interactions, ML models enable high-throughput screening of potential materials. However, data scarcity, model interpretability, and limited generalizability remain significant barriers to progress. This review critically evaluates the effectiveness and limitations of these predictive techniques, identifies unresolved issues, and discusses integrative research strategies that combine theory, simulation, and experimentation to accelerate discoveries in high-Tc superconductivity. This comparative analysis offers a roadmap for building interpretable, efficient, and scalable predictive models in the next phase of high-Tc materials research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".