Interpretable machine learning for optimizing piezoresistivity of carbon nanotube polymer nanocomposites for strain sensing
Bibliographic record
Abstract
This study presents an interpretable machine learning (ML) framework for optimizing the piezoresistivity of carbon nanotube (CNT) polymer nanocomposites. A training dataset is generated using a stochastic multiscale numerical model. This dataset is used to train three ML models: random forest, extreme gradient boosting (XGBoost), and artificial neural networks. The dataset includes eight key design variables: CNT length, CNT diameter, number of CNT conduction channels, intrinsic CNT conductivity, CNT volume fraction, polymer energy barrier height, Poisson’s ratio, and the applied strain, with the relative change in the resistance of the nanocomposites as the output variable. Shapley additive explanations (SHAP) analysis is employed to uncover structure-property relationships guiding the optimization of the nanocomposites for improved strain-sensing sensitivity. This framework enables accelerated modeling of CNT/polymer nanocomposites aiding in the design of materials with optimized sensing properties for strain-sensing applications.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".