Liang Zhang

The dissolution of 3d transition metals is a central bottleneck limiting the practical use of Pt‐alloy cathode catalysts in fuel cells. Although ordered intermetallic compounds (IMCs) have been explored for nearly 4 decades as a route to mitigate this instability, the durability benefit of structural ordering itself remains debated and has rarely been reliably validated under practical membrane electrode assembly (MEA) conditions. Herein, we design a phase‐diagram‐guided sequential annealing protocol that yields an ordered/disordered PtCo catalyst pair with closely matched particle size and composition, enabling the durability effect of structural ordering to be rigorously isolated. In practical MEAs, structural ordering does not translate into a significant H ₂ ‐air performance gain but markedly slows catalyst degradation. For example, after 90 000 cycles of accelerated durability test, the ordered catalyst loses only 28 mV at 1.5 A cm ⁻² , less than half that of its disordered counterpart (65 mV). Correlative in situ MEA diagnostics, electron microscopy, and online inductively coupled plasma mass spectrometry trace this enhanced durability to suppressed Co outward diffusion and dissolution. Machine‐learning‐accelerated theoretical calculations further rationalize this stabilization by revealing a higher thermodynamic penalty for Co surface segregation and a larger kinetic barrier for Co diffusion toward neighboring vacancies in the ordered IMC.

The dissolution of 3d transition metals is a central bottleneck limiting the practical use of Pt‐alloy cathode catalysts in fuel cells. Although ordered intermetallic compounds (IMCs) have been explored for nearly 4 decades as a route to mitigate this instability, the durability benefit of structural ordering itself remains debated and has rarely been reliably validated under practical membrane electrode assembly (MEA) conditions. Herein, we design a phase‐diagram‐guided sequential annealing protocol that yields an ordered/disordered PtCo catalyst pair with closely matched particle size and composition, enabling the durability effect of structural ordering to be rigorously isolated. In practical MEAs, structural ordering does not translate into a significant H ₂ ‐air performance gain but markedly slows catalyst degradation. For example, after 90 000 cycles of accelerated durability test, the ordered catalyst loses only 28 mV at 1.5 A cm ⁻² , less than half that of its disordered counterpart (65 mV). Correlative in situ MEA diagnostics, electron microscopy, and online inductively coupled plasma mass spectrometry trace this enhanced durability to suppressed Co outward diffusion and dissolution. Machine‐learning‐accelerated theoretical calculations further rationalize this stabilization by revealing a higher thermodynamic penalty for Co surface segregation and a larger kinetic barrier for Co diffusion toward neighboring vacancies in the ordered IMC.

The catalytic performance of single‐atom catalysts (SACs) is critically shaped by their local chemical environment, yet the role of structural features beyond the first coordination shell remains poorly understood. Here, we employ Ir single‐atom‐loaded ZnCo ₂ O ₄ spinel oxides with calcination‐tuned Co spin‐state distributions to elucidate their impact on the oxygen evolution reaction (OER). We find that the OER activity shows a quasi‐linear correlation with the fraction of high‐spin (HS) Co ³⁺ ions in the second coordination shell. Among the series, Ir–ZCO–600, dominated by HS Co ³⁺ , delivers outstanding OER performance, requiring overpotentials of only 256 mV (alkaline) and 302 mV (acidic) to achieve 10 mA cm ⁻² current density. Density functional theory calculations show that the Ir‐Co‐HS model exhibits a lower Ir d‐band center and weaker *OOH binding than the corresponding IS and LS models, consistent with the experimentally observed activity trend. These results suggest that the spin state of Co in the second coordination shell may serve as a relevant descriptor for tuning the catalytic behavior of oxide‐supported SACs.

High‐entropy alloys (HEAs), with their unique surface chemical disorder and rich active site distributions, hold great promise as electrocatalysts for hydrogen evolution. However, their vast compositional space poses a fundamental challenge for rational catalyst design. In this work, we propose a Hierarchical Site to Composition Machine Learning (HS2C‐ML) framework to accelerate the discovery of efficient HEA catalysts for alkaline hydrogen evolution reaction (HER). At the site level, a fine‐tuned, site‐resolved machine‐learning model rapidly predicts *H and *OH adsorption energies across complex HEA surfaces, enabling high‐throughput activity evaluation from adsorption‐energy distributions. At the composition level, a physics‐informed sparsifying operator algorithm establishes interpretable relationships between alloy composition and catalytic performance, allowing direct screening across thousands of compositions without explicit atomistic calculations. By screening ∼16,000 HEA compositions, we computationally prioritize a series of promising HEA candidates with favorable predicted alkaline HER activities, and the relevance of the predicted composition space is supported by consistency with selected literature reports and by the synthesis and electrochemical evaluation of one representative catalyst. This work provides a general and scalable strategy for data‐driven catalyst design in high‐dimensional HEA compositional spaces.

Rare-event molecular dynamics simulations are limited by the separation between atomic and activated timescales. Hyperdynamics accelerates these processes via bias potentials while preserving correct transition kinetics. The minimum-mode following (MMF) formulation provides a ridge-based bias construction but typically employs an identity approximation for the Jacobian together with local force evaluation, which introduces inconsistency in high-dimensional systems. Here, we develop a Jacobian-corrected MMF (J-MMF) method that improves both accuracy and consistency. The method introduces a path-informed semi-analytical Jacobian based on the sequential evolution of minimum-mode vectors and an orthogonal projection that removes spurious force components along the reaction coordinate, without additional force evaluations. Benchmark simulations of adatom diffusion on the Cu(100) surface (1-201 mobile atoms) demonstrate improved accuracy and robust, largely size-independent acceleration compared with standard MMF and bond-boost methods. J-MMF provides a scalable and consistent framework for rare-event simulations in complex atomistic systems.

For liquid organic hydrogen carriers (LOHCs), developing efficient hydrogen release strategies is crucial to advancing storage technologies. The methylcyclohexane (MCH)-toluene (TOL) system holds promise, yet its practical use remains limited by high dehydrogenation temperature, by-product formation, and catalyst deactivation. Here, machine-learning-assisted transition-state exploration, density functional theory calculations, and microkinetic simulations are combined to unravel the complex MCH dehydrogenation network, revealing the atomic-scale origin of structure sensitivity on platinum (Pt) catalysts. Pt(111) exhibits high selectivity toward TOL but limited activity due to its weak C−H activation capability at terrace sites; Pt(110), exposed with low-coordinated step sites, facilitate efficient C−H bond cleavage, yet Pt(110) strongly binds TOL, which drives its conversion into benzene (BZ) and promotes carbon production, thus impairing TOL selectivity; in contrast, Pt(211) achieves a balance between activity and selectivity, attributed to its moderate interaction with TOL through the cooperative effect of both terrace and step sites. Furthermore, we elucidate the influence of reaction conditions: elevating the temperature accelerates dehydrogenation kinetics, enhances product desorption, and alleviates coke and carbon deposition, while co-feeding hydrogen introduces surface *H species that suppress the accumulation of carbon intermediate, thereby improving product selectivity and extending catalyst durability. Collectively, these insights deepen the mechanistic understanding of LOHC dehydrogenation and provide guidance for the rational design of Pt-based catalysts.

Substituting homogeneous metal catalysts with recyclable heterogeneous metal catalysts has long been a goal in catalysis. However, most approaches yield heterogeneous metal catalysts that often exhibit relatively low stability and diminished catalytic activity under practical reaction conditions. Herein, we present an inorganic Rh catalyst obtained by confining subnanometer Rh clusters within a self-pillared zeolite structure. This catalyst design not only stabilizes the subnanometer Rh active sites but also facilitates efficient mass transport properties during solvent-free olefin hydroformylation reaction. Owing to the unique electronic properties of the Rh clusters and the optimal morphology of the zeolite support, the optimal catalyst achieves over 1,100,000 total turnovers across a broad substrate range under solvent-free conditions. This strategy can be extended to other metals confined within self-pillared zeolite, offering a general approach to circumvent the wettability issue of heterogeneous metal catalysts for solvent-free organic transformations.

Ionomer adsorption-induced Pt poisoning and high local oxygen transport resistance constitute two critical challenges preventing proton exchange membrane fuel cells from achieving low-Pt loadings. Yet, strategies concurrently mitigating both issues remain limited, making it challenging to achieve satisfactory performance in both kinetically controlled low-current-density and mass transport-controlled high-current-density regions. Herein, we show that this limitation can be addressed with a mesoporous carbon support design whose key lies in constructing a small mesopore-dominated structure while rationally controlling pore depth to a medium level. This design creates an ideal local reaction environment for Pt nanoparticles inside the mesopores, one that protects most Pt from ionomer poisoning while achieving efficient oxygen diffusion. Using the developed carbon support, we synthesize an intermetallic PtCo nanocatalyst that delivers competitive membrane electrode assembly performance at low-Pt cathode loading (0.1 mg_Pt cm^-2), with a H₂-air rated power density of 1.23 W cm^-2 at 0.67 V under heat-rejection-constrained conditions and a H₂-O₂ mass activity of 1.1 A mg_Pt^-1.