Multi-task Attention for Doped Thermoelectric Properties Prediction
Bibliographic record
Abstract
Abstract Improving the performance of thermoelectric (TE) materials is essential for their wider adoption in sustainable energy and cooling applications. Impurity doping is a common strategy for enhancing transport properties, yet synthesizing every possible TE composition is infeasible, and high-fidelity ab initio simulations remain computationally expensive. Machine learning offers a viable alternative by learning a cheaper surrogate model, but conventional approaches often struggle to capture the diverse doping effects due to compositional similarity between doped and undoped materials. To address this challenge, we adapt the existing Compositionally restricted attention-based Network ( CrabNet ) into a multi-task (MT) variant, MT CrabNet , which uses self-attention to implicitly learn composition-specific dopant effects. MT learning enables simultaneous predictions of seven TE transport properties, leveraging their interdependence to enhance performance. We also explore different strategies for encoding external variables such as temperature. Trained on an experimental dataset covering over 1400 unique compositions, MT CrabNet consistently achieves top performance, with significant improvements for electronic thermal conductivity and electrical conductivity. Compared to its single-task variant, MT CrabNet delivers up to 1.5× improvement in R 2 . This establishes MT CrabNet as a valuable tool for accelerating the screening and discovery of high-performance TE materials, with a design extensible to doped systems beyond the TE domain.We also share the code and dataset used in this work.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".