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PreprintCC BYChemRxiv

A Catalyst Discovery Workflow Coupling MLIP with MOBO for Nitrite-to-Hydroxylamine Electroreduction

  • Zhiyi Yu1,2
  • Huaiwen Zhang3,4
  • Weiming Zhao5,6
6 affiliations
  1. 1Ministry of Education; School of Chemical and Material Engineering
  2. 2Jiangnan University
  3. 3School of Computer Science and Technology
  4. 4Xidian University
  5. 5School of Geography and Environmental Management
  6. 6University of Waterloo
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Abstract

The selectivity of electrocatalytic nitrite reduction to hydroxylamine is governed by multiple coupled steps within the multi-electron nitrogen–oxygen reduction network, which cannot be represented simultaneously by a volcano relation based on a single adsorption descriptor. To address this limitation, a catalyst discovery workflow coupling machine-learned interatomic potentials (MLIPs) with multi-objective Bayesian optimization (MOBO) is developed. A small set of physical descriptors maps discrete oxide–dopant candidates onto a low-dimensional continuous representation, while four thermodynamic objectives describe the competing steps in NH2OH formation. The MLIP provides unified, low-cost structural and energy evaluation for oxide candidates. A Gaussian process surrogate model together with expected hypervolume improvement sequentially selects candidates from the discrete pool and updates the Pareto front within a fixed evaluation budget. The workflow integrates chemical objectives, energy evaluation, and sequential decision-making for catalyst screening in oxide systems.

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Cite

BibTeXRIS
BibTeX
@misc{yu2026catalyst,
  title = {{A Catalyst Discovery Workflow Coupling MLIP with MOBO for Nitrite-to-Hydroxylamine Electroreduction}},
  author = {Zhiyi Yu and Huaiwen Zhang and Weiming Zhao},
  howpublished = {ChemRxiv},
  year = {2026},
  month = sep,
  doi = {10.26434/chemrxiv.15009355/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009355/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - A Catalyst Discovery Workflow Coupling MLIP with MOBO for Nitrite-to-Hydroxylamine Electroreduction
AU  - Zhiyi Yu
AU  - Huaiwen Zhang
AU  - Weiming Zhao
PY  - 2026
DA  - 2026/09/23
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15009355/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009355/v1
AB  - The selectivity of electrocatalytic nitrite reduction to hydroxylamine is governed by multiple coupled steps within the multi-electron nitrogen–oxygen reduction network, which cannot be represented simultaneously by a volcano relation based on a single adsorption descriptor. To address this limitation, a catalyst discovery workflow coupling machine-learned interatomic potentials (MLIPs) with multi-objective Bayesian optimization (MOBO) is developed. A small set of physical descriptors maps discrete oxide–dopant candidates onto a low-dimensional continuous representation, while four thermodynamic objectives describe the competing steps in NH2OH formation. The MLIP provides unified, low-cost structural and energy evaluation for oxide candidates. A Gaussian process surrogate model together with expected hypervolume improvement sequentially selects candidates from the discrete pool and updates the Pareto front within a fixed evaluation budget. The workflow integrates chemical objectives, energy evaluation, and sequential decision-making for catalyst screening in oxide systems.
ER  -