Machine Learning-Predicted Ternary Molybdenum Chalcogenophosphides for High-Efficiency Hydrogen Evolution Catalysis
Bibliographic record
Abstract
The search for efficient and cost-effective alternatives to platinum-based catalysts for the alkaline hydrogen evolution reaction (HER) remains a formidable challenge, driving the need for innovative materials. In this study, we employed machine learning-driven moment tensor potentials in conjunction with particle swarm optimization to predict a new family of ternary molybdenum chalcogenophosphides, specifically Mo 2 SP and Mo 3 SP. Our calculations show that these materials exhibit robust thermodynamic, kinetic, and thermal stability at room temperature, as indicated by their low formation energies, phonon dispersion curves free of imaginary frequencies, and molecular dynamics simulations. Advanced HSE06 calculations further confirm their metallic nature in the bulk phase. We also investigated their size-dependent nucleation behavior, finding that Mo 2 SP and Mo 3 SP possess distinct crystallization pathways, with Mo 2 SP showing a lower nucleation barrier. Notably, the (100) facet of Mo 3 SP displays optimal hydrogen adsorption energies and lower water dissociation barriers compared to the (110) facet of Mo 2 SP, highlighting its superior catalytic efficiency in the Volmer step of HER. These findings provide a crucial foundation for the development of high-performance, low-cost catalysts based on ternary molybdenum chalcogenophosphides, offering a promising alternative for sustainable hydrogen production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".