Accurately predicting transport properties of porous fibrous materials by machine learning methods
Bibliographic record
Abstract
Abstract Machine learning algorithms trained on data gathered from stochastically generated gas diffusion layers (GDLs) were used to predict key transport properties that govern effective mass transport behaviour in polymer electrolyte membrane fuel cells. Specifically, we present the largest database in the present literature of stochastically generated fibrous GDL substrates (containing over 2000 unique materials) and the associated structural and transport properties determined via pore network modelling. Seven established machine learning algorithms were trained to predict the effective single‐phase permeability ( k sp ) and diffusivity ( D sp ), and the relative permeability ( k r ) and diffusivity ( D r ) of the generated materials using well‐defined material properties as input features. Gradient boosting regression (GBR), artificial neural network, and support vector regression were the best performing predictors of the single‐phase properties, all of which exhibited statistically insignificant differences in error. GBR provided the best prediction accuracy of relative transport properties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".