Data-Driven Design of High-Temperature-Resistant Polyimides Using Hierarchical Gaussian Process Regression
Bibliographic record
Abstract
Accurate prediction of Tg for polyimides (PIs) is essential for assessing material performance in high-temperature applications in aerospace, electronics, microelectronics, and flexible display technology. However, experimental measurements remain critically challenging due to the labor-intensive synthesis, conventional instrument limits, and time-consuming characterization processes. Meanwhile, force field limitations, timescale discrepancy, and validation difficulties exist in the prediction of Tg for PIs using molecular dynamic simulation. In this study, we introduce a hierarchical Gaussian process regression machine learning method that integrates prior knowledge to predict Tg for PIs with small-sample data sets. We employ RDKit for molecular descriptor calculation and feature selection. Twenty-one key descriptors are identified, and exceptional model performance with a coefficient of determination R 2 of 0.98/0.74 on the training/test set is achieved, surpassing conventional machine learning approaches. We further use Shapley additive explanations analysis to study the actionable insights for designing thermally stable PIs. The number of rotatable bonds and minimum partial charge act as dominant factors influencing Tg. Validations through experimental synthesis and molecular dynamics simulations confirm that the prediction errors are below 15%, while a Bayesian update strategy employing a radial basis function kernel corrected systematic underestimation in the high-Tg regime (>270 °C). This work provides a robust, validated Tg prediction tool, elucidates critical structure–property relationships, and establishes a transferable framework for data-driven materials design, advancing the development of high-performance polymers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".