Unveiling catalytic microenvironments: the convergence of conventional computations and machine learning paradigms
Year: 2026
Type: article
Abstract: Electrocatalysis is central to energy conversion and chemical transformations needed for carbon neutrality. Yet static structural models remain common for rationalizing catalytic performance, despite growing evidence that activity depends on dynamic active-site microenvironments. In this Review, we synthesize recent advances from three connected perspectives. First, we examine structural regulation strategies, including defect engineering, coordination engineering, crystal-facet control and heterointerface construction. These approaches tune local coordination environments, electronic structures and intermediate adsorption, thereby lowering reaction barriers and reshaping catalytic pathways. Second, we highlight grand-canonical density functional theory, constant-potential molecular dynamics and explicit interfacial simulations. These methods show how applied potential, electrolyte composition, solvation structure and surface reconstruction jointly determine operando active-site configurations. They further suggest that true active sites often arise as potential-stabilized, dynamically reconstructed states rather than idealized as-prepared structures. Third, interpretable machine learning and symbolic regression are emerging as tools for identifying physically meaningful descriptors that connect local structural and electronic microenvironments with catalytic activity. In parallel, neural network potentials extend atomistic simulations to the spatial and temporal scales needed to resolve evolving interfacial microenvironments. Large language models, AI agents and automated laboratories further create opportunities to integrate heterogeneous mechanistic knowledge with simulations and experimental feedback. By linking rational structural design, operando analysis and data-driven automation, these advances are moving electrocatalysis beyond empirical trial-and-error toward microenvironment-informed, mechanism-guided discovery under realistic reaction conditions.
Source: AI Agent
Language: English
Field: Energy
Domain: Physical Sciences
Open Access status: hybrid
Awards: G2975368776, G4678359439
OpenAlex ID: W7225395733