Dynamic Surface Reconstruction in Machine-Learning-Predicted Cu <sub>3</sub> MoP Governs Selective CO Electroreduction to C <sub>2</sub> <sup>+</sup> Products
Bibliographic record
Abstract
Dynamic surface reconstruction has emerged as a pivotal strategy for enhancing both activity and selectivity in electrochemical CO reduction (eCOR) to multicarbon (C 2 + ) products, key intermediates in sustainable fuel synthesis. In this study, we introduce a physics-informed, machine-learning-driven framework that integrates moment tensor potentials with a symmetry-guided ABC algorithm to systematically explore the structural landscape of ternary alloys. This data-centric approach identifies Cu 3 MoP as a thermodynamically favorable metallic phase, exhibiting robust dynamical stability as validated by phonon dispersion analysis and long-time-scale molecular dynamics simulations. Explicit modeling of the solid–liquid interface confirms that Cu 3 MoP retains structural integrity under experimentally relevant electrochemical conditions. Mechanistic investigation of the Cu 3 MoP(100) surface reveals a low onset potential of 0.19 eV for ethanol production and a moderate C–C coupling barrier of 0.31 eV, indicating kinetically accessible pathways toward C 2 + product formation. Under aqueous conditions, dynamic surface reconstruction induces Mo clustering and the emergence of Cu–Mo motifs, which modulate the electronic structure and redirect product selectivity from ethanol to ethylene. This interfacial restructuring also reorients water molecules, forming a structured hydration shell that stabilizes key reaction intermediates. Collectively, these findings establish Cu 3 MoP as a dynamically adaptive and highly selective electrocatalyst and demonstrate the effectiveness of integrating machine learning with atomistic simulations to accelerate the discovery of next-generation multicarbon eCOR systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".