Machine Learning-Assisted Multiobjective Optimization of the Catalyst Layer in a Proton Exchange Membrane Water Electrolyzer
Bibliographic record
Abstract
Catalyst layer (CL) optimization plays a crucial role in enhancing the performance of proton exchange membrane water electrolyzer (PEMWE). Herein, a multiobjective optimization framework was developed for fast PEMWE performance prediction and CL optimization toward increased current density, mass activity, and temperature uniformity. In this framework, a data-driven PEMWE performance prediction model is first constructed by incorporating the eXtreme gradient boosting algorithm into the 2D two-phase nonisothermal PEMWE agglomerate model. Using the PEMWE performance prediction model as a surrogate model, the second generation of nondominated sorted genetic algorithm was implemented into the CL parameter multiobjective optimization process. Accordingly, three sets of optimal CL parameters were selected using the technique for order preference by similarity to an ideal solution method. The performance of PEMWE with optimal CL parameters is significantly improved compared with that of the benchmark PEMWE physical model. The framework and results of this study provide important guidance for the optimization design of high-performance PEMWE.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".