A Multiobjective Closed-loop Approach Towards Autonomous Discovery of Electrocatalysts for Nitrogen Reduction
Bibliographic record
Abstract
Electrocatalyst discovery is an inherently multiobjective challenge that can benefit from closed-loop approaches towards acceleration. However, previous computational closed-loop efforts for electrocatalysis often focus on a single objective to be optimized. Here we demonstrate a multiobjective closed-loop strategy towards identifying single-atom alloy (SAA) electrocatalysts for nitrogen reduction considering activity, stability, and cost. Candidates were autonomously selected via a multiobjective scoring approach, as implemented in our AutoCat software, and evaluated using a high-throughput density functional theory pipeline. We discuss the implications of our scoring system formulation and show its ability to efficiently explore the SAA design space. We also propose a multiobjective method to rank evaluated candidates balancing the three target metrics, with Zr$_1$Cr, Hf$_1$Cr, Ag$_1$Re, Au$_1$Re, and Ti$_1$Fe ranking the highest. The inclusion of Hf is of particular interest as it is more commonly found within the context of molecular catalysts, which are similar yet distinct from SAAs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".