Machine learning‐based model for predicting the material properties of nanostructured aerogels
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 Data‐driven modeling in material science rose to prominence in the last decade, and various supervised and unsupervised machine learning techniques have been employed for material development and deriving insights for decision‐making purposes. In this context, machine learning can have prominent importance in the field of nanostructured aerogels for accelerated materials design and material properties prediction. Current attempts rely only on experimental approach, which have inherent shortcomings, including inefficiency due to the prolonged synthesis process, and necessity of analyzing microstructure and properties. In order to address the challenges associated with the traditional experimental approach, in this study, an artificial neural network (ANN) is employed to predict the material properties of nanostructured aerogels. Polyimide (PI) organic aerogels are selected for this purpose. Through understanding the contributing material and processing factors in PI aerogel synthesis, a dataset is prepared. Data preprocessing is performed, and through hyperparameter tuning, ANN is constructed and trained for a given dataset. Various material properties are predicted, including compressive modulus, density, and porosity. Results show that ANN is trained with high accuracy, which demonstrates the versatility and accuracy of model in materials properties prediction. This study can therefore pave the way for establishing a platform for data‐driven materials innovation.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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