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Dynamics and kinetics exploration of the oxygen reduction reaction at the Fe–N<sub>4</sub>/C–water interface accelerated by a machine learning force field

Qinghan Yu, Pai Li, Xing Ni, Youyong Li, Lu Wang

2025Chemical Science19 citationsDOIOpen Access PDF

Abstract

A machine learning force field workflow efficiently accelerates ab initio molecular simulations and reveals the significance of solvent configurations in electrocatalytic simulations.

Topics & Concepts

Interface (matter)Reduction (mathematics)Oxygen reduction reactionOxygenField (mathematics)Kinetic energyFuel cellsDynamics (music)Reaction dynamicsMechanism (biology)Reaction mechanismComputer scienceChemistryChemical physicsChemical engineeringEngineeringPhysical chemistryPhysicsCatalysisMoleculeMathematicsClassical mechanicsPure mathematicsElectrodeAdsorptionAcousticsGibbs isothermQuantum mechanicsBiochemistryOrganic chemistryGeometryElectrochemistryMachine Learning in Materials ScienceElectrocatalysts for Energy ConversionFuel Cells and Related Materials
Dynamics and kinetics exploration of the oxygen reduction reaction at the Fe–N<sub>4</sub>/C–water interface accelerated by a machine learning force field | Litcius