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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".