Aug 2026· Chemical Communications· Vol 62, pp. 16587-16602· 0 citations
Medicine
Abstract
Porous catalytic materials, including metal-organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, and porous carbons, provide structurally defined microenvironments for controlling reactivity and are increasingly being investigated in electrocatalysis. In electrochemical systems, potential-dependent adsorption energetics, electric double-layer structure, solvent effects, and mass transport within confined pores introduce additional layers of complexity beyond conventional heterogeneous catalysis. Decoding structure-reactivity relationships under such conditions therefore requires representation strategies that are explicitly aligned with reaction-relevant states. This review summarizes recent data-driven strategies used to interrogate porous catalysts, organized around three themes: (i) chemically informed, descriptor-based models that connect local structure to activity/selectivity/stability; (ii) graph-based representations that encode connectivity and topology to learn reactivity-relevant motifs; and (iii) multimodal and transferable learning approaches that integrate structural, spectroscopic and energetic information across material classes. Representative examples across MOFs/COFs, zeolites and porous carbons are discussed, with emphasis on studies that pair modelling with mechanistic reasoning and targeted experiments. Key bottlenecks remain, including the scarcity of reaction-resolved electrochemical datasets, limited treatment of dynamic restructuring under bias, and mismatches between computational descriptors and experimentally measurable observables. We conclude by outlining priorities for reaction-relevant descriptor design and model-experiment feedback loops to accelerate porous electrocatalyst development for sustainable chemical transformation.
Metal-organic frameworks (MOFs) and MOF-like porous materials exhibit vast structural diversity and support critical applications in gas storage, separations, and catalysis. Predictive modeling remains difficult because their structure-property relationships are multiscale and cage-like, governed by both local chemical environments and global pore-network topology. These challenges, together with sparse and unevenly distributed labeled data, hinder generalization across material families. We develop an interaction topology theory and propose the interaction topological transformer (ITT), a data-efficient framework that captures materials information across multiple scales and levels, including structural, elemental, atomic, and pairwise-elemental organization. ITT extracts scale-aware features reflecting both compositional and relational structures in complex porous frameworks and integrates them through a transformer architecture for joint reasoning across scales. Using self-supervised pretraining on more than 0.6 million unlabeled structures followed by supervised fine-tuning, ITT achieves accurate, transferable, state-of-the-art predictions for adsorption, transport, and stability properties across 17 tasks, providing a principled and scalable strategy for learning-guided discovery in diverse MOF-like materials.
Dual-atom catalysts (DACs) provide a powerful platform for oxygen electrocatalysis, yet rational design remains limited by the lack of transferable mechanistic principles. Machine learning (ML) has the potential to address this gap, yet its role in mechanistic discovery remains largely underexplored despite its wide use in catalyst screening. Here, using extended phthalocyanines (M1M2-ePc), we establish an integrated DFT-ML-experiment framework that maps catalytic performance onto an interpretable electronic landscape. Screening 81 DFT-computed and 360 ML-predicted metal pairs identifies FeM-ePc as a promising bifunctional catalyst family. Notably, SHapley Additive exPlanations (SHAP) analysis highlights the importance of electronic background and the key role of the secondary metal in regulating catalytic activity. First-principles calculations further uncover a cooperative dual-descriptor mechanism, in which d-band center and charge transfer jointly govern bifunctional activity. Combining LASSO with SISSO yields compact analytical formulas that quantitatively reproduce ηORR and ηOER, providing interpretable descriptors for DACs. Guided by these findings, FeCo-ePc-L with atomically dispersed Fe-Co sites was synthesized to experimentally examine the ML-guided prediction. This work highlights the utility of interpretable ML for mechanistic discovery in DACs by revealing role-asymmetric electronic cooperation between paired metal centers.
Shao-Bo Jia, Lu Yang, Chou Wu et al.· Advances in Materials· 0 citations
Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.
Swetarekha Ram, Shalini Tomar, S. Bhattacharjee· Chemical Communications· 0 citations
A mechanism-driven approach to alleviate data dependence and develop a multisource transfer learning (MS-TL) framework that leverages the knowledge embedded in abundant adsorption data sets while accurately capturing local structural dependence, enabling a deep fusion of multidimensional thermodynamic knowledge while preserving local structural information.
Wangqiang Lin, Huiyan Zhang, Jinxin Sun et al.· Journal of the American Chem...· 0 citations
Metal–organic frameworks (MOFs) have emerged as versatile platforms for heterogeneous catalysis, owing to their structural tunability, well-defined active sites, and tailorable pore environments. Achieving efficient hydrogenation of complex molecules under mild conditions, however, remains challenging because the catalytic performance is highly sensitive to the local electronic and structural microenvironment of active sites. The stepwise hydrogenation of dicyclopentadiene (DCPD), which involves multiple unsaturated bonds and competing reaction pathways, provides a representative model system for elucidating these structure–activity relationships.
Over the past decade, MOF-based catalysts, including pristine frameworks, single-atom catalysts (SACs), and nanocluster/nanoparticle-loaded systems, have shown significant potential for hydrogenation reactions. Nevertheless, rational catalyst design remains difficult because framework topology, electronic structure, defect distribution, and metal–support interactions are strongly coupled across multiple length scales. As a result, establishing generalizable design principles through empirical approaches alone can be challenging.
In this Account, we present an integrated strategy for the rational design of MOF-based catalysts for DCPD hydrogenation by combining computational chemistry with artificial intelligence (AI). This framework bridges atomistic-level mechanistic understanding with synthesis optimization, enabling precise control of the catalytic performance.
At the atomic and electronic levels, density functional theory (DFT) calculations and descriptor-based analyses reveal key mechanistic factors governing hydrogen activation, charge redistribution, and reaction energetics. These studies guide the rational design of frustrated Lewis pairs (FLPs) in pristine MOFs through the optimization of charge distributions for heterolytic H2 cleavage. Extending this concept to SACs, electronic structure engineering demonstrates how metal–support interactions and d-band modulation regulate the adsorption behavior and reaction pathways. At larger scales, investigations of nanometer clusters and nanoparticle-loaded systems further highlight the critical role of interfacial electronic structures, including electron transfer and hydrogen spillover, in enhancing the catalytic activity.
Complementing these mechanistic insights, machine learning (ML) and large language model (LLM) driven approaches are employed to address the complexity of MOF synthesis. Data-driven models efficiently explore high-dimensional synthetic parameter spaces, enabling defect engineering and multiobjective optimization of catalytic properties. Furthermore, the integration of LLMs with retrieval-augmented generation (RAG) supports closed-loop synthesis frameworks in which the experimental design, data analysis, and knowledge extraction are autonomously coordinated. These AI-assisted methodologies significantly accelerate catalyst optimization and the precise regulation of active-site structures.
Through this integrated computational and AI-assisted strategy, MOF-based catalysts achieve complete conversion and high selectivity for DCPD hydrogenation under mild conditions. More broadly, this work establishes a generalizable paradigm in which mechanistic modeling and data-driven methodologies are synergistically integrated to guide the rational design of heterogeneous catalysts.
Yi-Fan Zhang, Zuoshuai Xi, Zhimeng Liu et al.· Accounts of Materials Resear...· 0 citations
Precise monitoring of nuclear off-gases requires sensing systems that convert molecular adsorption events into reliable, quantifiable signals in complex environments. However, studies of metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) remain fragmented, and the links among gas-phase physicochemical properties, adsorption thermodynamics, electronic-structure evolution, and device-level responses are not yet fully understood. Here, a unified, mechanism-driven framework is established to correlate adsorption and signal transduction across multiple scales. The effects of key molecular descriptors, including polarizability, dipole moment, and quadrupole moment, on gas adsorption behaviour in ordered porous frameworks are analysed. Adsorption-driven charge transfer modulates the density of states, Fermi level, and work function, thereby altering carrier concentration and transport properties. The review integrates grand canonical Monte Carlo simulations, density functional theory calculations, machine-learning methods, and in situ or operando spectroscopy to examine correlations among thermodynamic parameters, electronic-structure changes, experimental signatures, and sensing performance. Furthermore, the distinct structural features of MOFs and COFs are discussed within a localisation-delocalisation continuum. The effects of radiation, humidity, and temperature on framework evolution, adsorption behaviour, electronic perturbation, and signal-transduction mode are also discussed. This work provides a unified basis for understanding gas-sensing mechanisms in porous materials and offers guidance for the rational design, experimental validation, and practical deployment of high-performance MOF/COF sensing systems for complex nuclear environments.
Cheng-En Luo, Yan Lu, Wei-Jie Lian et al.· Physical Chemistry, Chemical...· 0 citations
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