Stochastic Nonlinear Optimization for Robust Design of Catalysts
Bibliographic record
Abstract
Computational methods for designing an optimal catalyst have recently received much attention, especially for energy-related applications. What is lacking in the previous methods is an explicit method to handle uncertainties in the complex models used, so that a robust design is achieved. This work proposes a stochastic optimization method for designing a robust catalyst. In particular, reactions involved in catalytic decomposition of ammonia are presented, and uncertainties associated with experimental determination of kinetic parameters are represented as exogenous variables with assumed probability distributions. The problem is formulated in terms of finding the optimal binding energies that maximize conversion in a microreactor. The resulting stochastic optimization problem is nonlinear, and involves the expectation operator as well as integration in the objective function. This difficult optimization problem is tackled by a population sample based approach, referred to as particle swarm optimization. The results show that the value of solving the stochastic problem is significant, and that it can provide a more robust solution compared to the certainty equivalence approach.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".