The active sites of alloy catalysts may emerge only under reaction conditions, yet how reactive atmospheres select these sites remains unresolved. Here, we reveal how reactive atmospheres reorganize complex alloy surfaces into functional active-state distributions by using a machine-learning-potential-accelerated multiscale framework. Using selective acetylene hydrogenation on Pd–Ag alloys as a model reaction, we show that adsorbates reverse the intrinsic Ag-segregation tendency, enrich Pd in the outermost layer, and generate a distribution of Pd3 hollow ensembles with distinct second-shell coordination environments. These dynamically formed ensembles, rather than the as-prepared isolated Pd sites, govern the calculated activity and selectivity trends. Ensemble-resolved energetics and kinetics identify the adsorption free-energy gap between ethylene and acetylene as a predictive descriptor that captures the activity-selectivity trade-off and defines an optimal window balancing acetylene hydrogenation and ethylene desorption. Extending the analysis across multiple alloy families further reveals a general scaling relationship in which adsorbate-driven self-organization is governed by adsorption asymmetry and alloy stability. These results establish dynamic ensemble selection as a transferable framework for understanding and designing adaptive alloy catalysts, shifting catalyst optimization from static structural descriptors toward reaction-condition-directed control of active-state distributions.
While the rich diversity of surface sites on high-entropy alloys (HEAs) is essential for tuning electrocatalytic activity, the coverage-dependent lateral interactions that shape reactive interfaces are often neglected in theoretical studies. Here, we develop a machine learning interatomic potential (MLIP)-enabled framework to model the oxygen reduction reaction (ORR) within an Ag-Ir-Ru-Pd-Pt-Cu-Rh-Re alloy composition space. By tracking the binding strengths of O* and OH* intermediates during competitive coadsorption on crowded surfaces, this framework highlights the key role of lateral interactions, including attractive hydrogen-bond networks and electrostatic repulsion, in evaluating electrocatalytic activity. Incorporating these coverage-induced effects improves agreement with reported PtIr and AgPd composition-activity trends relative to an isolated-site baseline. We further show that increasing compositional complexity within the studied alloy space can amplify lateral repulsion under finite-coverage conditions, broadening binding strength distributions and reducing the population of optimal active sites. The competition between local electronic optimization and coverage-dependent lateral interactions gives rise to a volcano-shaped activity-entropy relationship, offering guidance for the rational design of HEA-based ORR electrocatalysts.
Pengfei Hou, Jin-Cheng Liu· Journal of the American Chem...· 1 citation
The determination of surface configuration is essential for understanding catalytic reactivity, yet these active states cannot be reliably inferred from bulk phase diagrams. In this work, we develop a machine-learning-accelerated molecular dynamics (ML-MD) framework on the Cu-Ag system to predict and control surface structure, identifying two competing descriptors: surface energy and adsorbate binding energy. We show that Ag's lower surface energy dominates in vacuum, forming a passive sheath, whereas a CO atmosphere reverses segregation and exposes active Cu sites through lateral phase separation. Decoupling the thermodynamic driving force from the kinetic diffusion barrier, we find that stability emerges not at near-equimolar ratios where segregation is strongest but rather at dilute compositions where weak driving forces and high kinetic barriers coincide. This framework offers a generalizable strategy for balancing activity and stability in metastable catalyst design.
Unknown authors· Journal of Physical Chemistr...· 0 citations
High-entropy oxides (HEOs) offer vast compositional design space for discovering emergent functionalities, yet their controlled nanoscale synthesis remains challenging. Here, we develop a generalizable colloidal strategy that enables precision synthesis of HEO nanocrystals with compositions spanning quinary to septenary systems. Mechanistic studies reveal that differences in precursor reactivity drive a multistage growth pathway and that cooperative multimetal chemistry─where one metal initiates single-phase nucleation and a vacancy-forming metal promotes cation redistribution during growth─enables homogeneous multication incorporation. The resulting rocksalt HEO nanocrystals can be transformed into spinel phases and exhibit excellent oxygen-evolution reaction activity. Machine-learning-accelerated theoretical analysis and operando characterization identify Co-Co bridge sites on spinel {111} facets as the dominant oxygen-evolution active motifs operating through a lattice oxygen-mediated mechanism. These findings establish guiding principles for controlling nucleation, cation mixing, and active-site formation in compositionally complex oxides, enabling the rational design and data-driven optimization of high-entropy materials.
Baixu Zhu, Liping Liu, Alex N Butrum-Griffith et al.· Journal of the American Chem...· 0 citations
High-entropy oxides (HEOs), which contain multiple cations with diverse valence states and highly disordered local environments, offer a much broader compositional and active-site space than conventional single-component oxides. This diversity creates opportunities for tailoring catalytic properties, but it also makes rational design considerably more difficult. The formation and stability of HEOs phases depend on the combined effects of configurational entropy, mixing enthalpy, oxidation potential, valence compatibility, and synthesis conditions. Meanwhile, their catalytic behavior is often controlled by oxygen vacancies, surface segregation, in situ reconstruction, and dynamically evolving active sites under operating conditions. Artificial intelligence (AI) provides a promising means of managing this complexity by learning relationships among composition, structure, defects, adsorption behavior, and catalytic performance. This review summarizes the major challenges in HEOs catalyst design, discusses data representation and model selection, and examines AI applications in phase stability prediction, hydrogen production, oxygen evolution, thermal catalysis, photocatalysis, and electrocatalyst discovery. It further emphasizes the need to move beyond single-property prediction toward multi-objective optimization and closed-loop integration of AI, DFT, MLIP-based sampling, operando characterization, and experimental validation.
Ying He, Hai-Yang Cheng, Tong Zhou et al.· Advances in Materials· 0 citations
The thermodynamic−kinetic origin of why alloying is a prerequisite for interstitial carbon playing a promotional role in selective acetylene hydrogenation over Ni-based catalysts remains elusive. Moreover, catalytic mechanisms are frequently studied on pristine or bare surface models, overlooking the realistic surface states and coverage effects under operando conditions. Herein, we establish a realistic surface-state research paradigm, integrating global optimization, ab initio thermodynamic phase diagrams, constrained ab initio molecular dynamics, and coverage self-consistent calculations. We reveal that pure Ni thermodynamically rejects subsurface carbon (ΔEinc = −0.41 eV) with a prohibitively high kinetic barrier (3.75 eV), while Ga/Zn alloying switches the thermodynamics to favorable (ΔEinc = −3.61 eV) and lowers the penetration barrier by ∼0.95 eV via lattice expansion and electronic activation. Coverage-dependent calculations demonstrate that adsorbate−adsorbate interactions adjust the free energy profiles, and the coverage self-consistent microkinetic modeling and reactor simulations align well with experimental trends of Ni-based catalysts. The excellent performance of Ni3GaCx and Ni3ZnCx originates from interstitial carbon within the alloy matrix. Synergistic ligand and geometric effects tuning Ni-based carbic to the summit of a predictive selectivity volcano that simultaneously suppresses overhydrogenation and oligomerization, where electronic modulation optimizes intermediate adsorption strength while adjacent Ni atoms stretching creates selective advantage sites. This work transforms the understanding of interstitial carbon from a phenomenological observation into a quantitative, descriptor-based design map that can guide future rational catalyst design via controlled interstitial modification engineering.
Xin Bai, Xiaomeng Chen, Lanyu Li et al.· ACS Catalysis· 0 citations
The distribution of metals in alloy nanoparticles is a key parameter in their electrochemical performance. Here, we show that for Pt-Au alloys this distribution dynamically responds to the electrochemical environment. Using electrochemical X-ray photoelectron spectroscopy, we find that the surface composition of the alloy can range from stoichiometric to Pt-enriched, depending on the applied potential. The driving factors for this restructuring are the differences in electronegativity and mobility between Pt and Au. At potentials below 1.5 VRHE, the slightly more reactive (less electronegative) Pt shows surface enrichment, which slowly increases as the potential is raised and increasingly oxidizing adsorbates are formed. Above 1.5 VRHE, oxidation of the more mobile Au atoms becomes possible, which is kinetically favored over Pt oxidation, thereby driving Au toward the surface. All surface dynamics are found to be highly reversible, even at room temperature. This facile rearrangement suggests that dynamic restructuring of alloy nanoparticles occurs in a wide range of conditions, and should therefore be taken into account in alloy electrocatalyst design.
J. S. D. Rodriguez, Hassan Javed, Kees Kolmeijer et al.· Small· 0 citations
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