Recent strides in artificial intelligence for predicting thermoelectric properties and materials discovery
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
Abstract Machine learning models as part of artificial intelligence have enjoyed a recent surge in answering a long-standing challenge in thermoelectric materials research. That challenge is to produce stable, and highly efficient, thermoelectric materials for their application in thermoelectric devices for commercial use. The enhancements in these models offer the potential to identify the best solutions for these challenges and accelerate thermoelectric research through the reduction in experimental and computational costs. This perspective underscores and examines recent advancements and approaches from the materials community in artificial intelligence to address the challenges in the thermoelectric area. Besides, it explores the possibility for these advancements to surpass existing limitations. Additionally, it presents insights into the material features influencing model decisions for thermoelectric property predictions and in some cases new thermoelectric material discovery. In the end, the perspective addresses current challenges and future potential studies beyond classical ML studies for thermoelectric research.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it