Machine learning‐based model for predicting the material properties of nanostructured aerogels
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.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".