A Catalyst Discovery Workflow Coupling MLIP with MOBO for Nitrite-to-Hydroxylamine Electroreduction
Year: 2026
Type: preprint
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.
Source: ChemRxiv
Authors: Zhiyi Yu, Huaiwen Zhang, Weiming Zhao
Institutions: Jiangnan University, Xidian University, University of Waterloo
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214092726