Unveiling future superconductors through machine learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The recent discovery of superconductivity above 200 K in hydrides of sulfur and lanthanum under high pressure marked a significant advance toward the realization of room-temperature superconductivity. While binary hydrides have almost been completely studied theoretically, experimental evidence suggests that the next breakthrough in finding high-temperature and low-pressure limits is likely connected with ternary and higher hydrides. Unlike the traditional synthesis-test-repeat approach, experimental discovery of superhydrides under high pressure often follows prior theoretical predictions. In this Minireview, we describe how various artificial intelligence schemes enable and enrich each stage of the discovery cycle of superhydrides and new developments made toward predicting ternary and higher hydrides. As a new enabling tool, machine learning-informed material simulation is still making its way into this field but is already playing an essential role in augmenting the prediction of new superhydrides through automated and iterative machine-learning processes. The review concludes with a perspective on outstanding challenges and possible future developments in the field.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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 it