Heterogeneous catalysis from structure to activity via SSW-NN method

Stability and Dynamics of Zeolite-Confined Gold Nanoclusters

Nanoengineered metal@zeolite materials have recently emerged as a promising class of catalysts for several industrially relevant reactions. These materials, which consist of small transition metal nanoclusters confined within three-dimensional zeolite pores, are interesting because they show higher stability and better sintering resistance under reaction conditions. While several such hybrid catalysts have been reported experimentally, key questions such as the impact of the zeolite frameworks on the properties of the metal clusters are not well understood. To address such knowledge gaps, in this study, we report a robust and transferable machine learning-based potential (MLP) that is capable of describing the structure, stability, and dynamics of zeolite-confined gold nanoclusters. Specifically, we show that the resulting MLP maintains ab initio accuracy across a range of temperatures (300-1000 K) and can be used to investigate time scales (>10 ns), length scales (ca. 10,000 atoms), and phenomena (e.g., ensemble-averaged stability and diffusivity) that are typically inaccessible using density functional theory (DFT). Taken together, this study represents an important step in enabling the rational theory-guided design of metal@zeolite catalysts.

Data-driven discovery of active phosphine ligand space for cross-coupling reactions

The design of highly active catalysts is a main theme in organic chemistry, but it still relies heavily on expert experience. Herein, powered by machine-learning global structure exploration, we forge a Metal-Phosphine Catalyst Database (MPCD) with a meticulously designed ligand replacement energy metric, a key descriptor to describe the metal-ligand interactions. It pushes the rational design of organometallic catalysts to a quantitative era, where a ±10 kJ mol-1 window of relative ligand binding strength, a so-called active ligand space (ALS), is identified for highly effective catalyst screening. We highlight the chemistry interpretability and effectiveness of ALS for various C-N, C-C and C-S cross-coupling reactions via a Sabatier-principle-based volcano plot and demonstrate its predictive power in discovering low-cost ligands in catalyzing Suzuki cross-coupling involving aryl chloride. The advent of the MPCD provides a data-driven new route for speeding up organometallic catalysis and other applications.

A human-machine interface for automatic exploration of chemical reaction networks

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.

Abundance of Low-Energy Oxygen Vacancy Pairs Dictates the Catalytic Performance of Cerium-Stabilized Zirconia

Cerium-stabilized zirconia (Ce1-xZrxOy, CZO) is renowned for its superior oxygen storage capacity (OSC), a key property long believed to be beneficial to catalytic oxidation reactions. However, 50% Ce-containing CZO recorded with the highest OSC has disappointingly poor performance in catalytic oxidation reactions compared to those with higher Ce contents but lower OSC ability. Here, we employ global neural network (G-NN)-based potential energy surface exploration methods to establish the first ternary phase diagram for bulk structures of CZO, which identifies three critical compositions of CZO, namely, 50, 60, and 80% Ce-containing CZO that are thermodynamically stable under typical synthetic conditions. 50% Ce-containing CZO, although having the highest OSC, exhibits the lowest O vacancy (Ov) diffusion rate. By contrast, 60% Ce-containing CZO, despite lower OSC (33.3% OSC compared to that of 50% Ce-containing CZO), reaches the highest Ov diffusion ability and thus offers the highest CO oxidation catalytic performance. The physical origin of the high performance of 60% Ce-containing CZO is the abundance of energetically favorable Ov pairs along the ⟨110⟩ direction, which reduces the energy barrier of Ov diffusion in the bulk and promotes O2 activation on the surface. Our results clarify the long-standing puzzles on CZO and point out that 60% Ce-containing CZO is the most desirable composition for typical CZO applications.

Machine learning facilitating the rational design of nanozymes

As a component substitute for natural enzymes, nanozymes have the advantages of easy synthesis, convenient modification, low cost, and high stability, and are widely used in many fields. However, their application is seriously restricted by the difficulty of rapidly creating high-performance nanozymes. The use of machine learning techniques to guide the rational design of nanozymes holds great promise to overcome this difficulty. In this review, we introduce the recent progress of machine learning in assisting the design of nanozymes. Particular attention is given to the successful strategies of machine learning in predicting the activity, selectivity, catalytic mechanisms, optimal structures and other features of nanozymes. The typical procedures and approaches for conducting machine learning in the study of nanozymes are also highlighted. Moreover, we discuss in detail the difficulties of machine learning methods in dealing with the redundant and chaotic nanozyme data and provide an outlook on the future application of machine learning in the nanozyme field. We hope that this review will serve as a useful handbook for researchers in related fields and promote the utilization of machine learning in nanozyme rational design and related topics.

In Situ Structure of a Mo-Doped Pt-Ni Catalyst during Electrochemical Oxygen Reduction Resolved from Machine Learning-Based Grand Canonical Global Optimization

Pt-Ni alloy is by far the most active cathode material for oxygen reduction reaction (ORR) in the proton-exchange membrane fuel cell, and the addition of a tiny amount of a third-metal Mo can significantly improve the catalyst durability and activity. Here, by developing machine learning-based grand canonical global optimization, we are able to resolve the in situ structures of this important three-element alloy system under ORR conditions and identify their correlations with the enhanced ORR performance. We disclose the bulk phase diagram of Pt-Ni-Mo alloys and determine the surface structures under the ORR reaction conditions by exploring millions of likely structure candidates. The pristine Pt-Ni-Mo alloy surfaces are shown to undergo significant structure reconstruction under ORR reaction conditions, where a surface-adsorbed MoO₄ monomer or Mo₂O _x dimers cover the Pt-skin surface above 0.9 V vs RHE and protect the surface from Ni leaching. The physical origins are revealed by analyzing the electronic structure of O atoms in MoO₄ and on the Pt surface. In viewing the role of high-valence transition metal oxide clusters, we propose a set of quantitative measures for designing better catalysts and predict that six elements in the periodic table, namely, Mo, Tc, Os, Ta, Re, and W, can be good candidates for alloying with PtNi to improve the ORR catalytic performance. We demonstrate that machine learning-based grand canonical global optimization is a powerful and generic tool to reveal the catalyst dynamics behavior in contact with a complex reaction environment.

Rational design of MoS2-supported Cu single-atom catalysts by machine learning potential for enhanced peroxidase-like activity

Two-dimensional molybdenum disulfide (2D-MoS₂)-supported single atom nanomaterials with enhanced enzyme-like activities are potential substitutes for natural enzymes due to their huge specific surface areas, ease of decoration, high catalytic activity and high catalytic stability. However, their catalytic mechanism remains unclear, making the rational design of nanozymes difficult to achieve. Herein, the mechanisms have been explored to enhance the peroxidase-like activity of MoS₂ for H₂O₂ decomposition. Global neutral network (G-NN) potentials were constructed to accurately and quickly illustrate the mechanisms of MoS₂ catalysts and their surface modifications. The high peroxidase-like activity of the MoS₂-supported Cu single atom catalyst with sulfur vacancy (Cu@MoS₂-Vs) in acidic conditions was systematically evaluated using the trained G-NN potential and density functional theory (DFT), as well as experimental validation. Further analysis of the geometric and electronic properties of pivotal stationary structures revealed the enhanced electron transfer process for high catalytic performance with the modulation of the Cu single atom loading, sulfur vacancy engineering and the surrounding acidic and alkaline environment regulation on the MoS₂ basal plane. The results also showed that Cu@MoS₂-Vs in an acidic environment exhibited the highest peroxidase-like activity. This work is expected to provide broad implications for the rational design of substrate-supported single-atom catalysts with superior performance and lower cost by surface modification and acidic and alkaline environment regulation.

Enhancing the Performance of Global Optimization of Platinum Cluster Structures by Transfer Learning in a Deep Neural Network

The global optimization of metal cluster structures is an important research field. The traditional deep neural network (T-DNN) global optimization method is a good way to find out the global minimum (GM) of metal cluster structures, but a large number of samples are required. We developed a new global optimization method which is the combination of the DNN and transfer learning (DNN-TL). The DNN-TL method transfers the DNN parameters of the small-sized cluster to the DNN of the large-sized cluster to greatly reduce the number of samples. For the global optimization of Pt₉ and Pt₁₃ clusters in this research, the T-DNN method requires about 3-10 times more samples than the DNN-TL method, and the DNN-TL method saves about 70-80% of time. We also found that the average amplitude of parameter changes in the T-DNN training is about 2 times larger than that in the DNN-TL training, which rationalizes the effectiveness of transfer learning. The average fitting errors of the DNN trained by the DNN-TL method can be even smaller than those by the T-DNN method because of the reliability of transfer learning. Finally, we successfully obtained the GM structures of Pt_n (n = 8-14) clusters by the DNN-TL method.

Coupled oxygen desorption and structural reconstruction accompanying reduction of copper oxide

Understanding structural transformation and phase transition accompanying reactions in a solid as a catalyst or oxygen carrier is important to the design and optimization of many catalytic or chemical looping reaction processes. Herein, we combined density functional theory calculation with the stochastic surface walking global optimization approach to track the structural transformation accompanying the reduction of CuO upon releasing oxygen. We then used machine learning (ML) methods to correlate the structural properties of CuO_x with varying x. By decomposing a reduction step into oxygen detachment and structural reconstruction, we identified two types of pathways: (1) uniform reduction with minimal structural changes; (2) segregated reduction with significant reconstruction. The results of ML analysis showed that the most important feature is the radial distribution functions of Cu-O at a percentage of oxygen vacancy [C(O_V)] < 50% and Cu-Cu at C(O_V) > 50% for CuO_x formation. These features reflect the underlying physicochemical origin, i.e., Cu-O breaking and Cu-Cu formation in the respective stage of reduction. Phase diagram analysis indicates that CuO will be reduced to Cu₂O under a typical oxygen uncoupling condition. This work demonstrates the complexity of solid structural transformation and the potential of ML methods in studying solid state materials involved in many chemical processes.

Elucidation of Cu-Zn Surface Alloying on Cu(997) by Machine-Learning Molecular Dynamics

The Cu-Zn surface alloy has been extensively involved in the investigation of the true active site of Cu/ZnO/Al2O3, the industrial catalyst for methanol synthesis which remains under controversy. The challenge lies in capturing the interplay between the surface and reaction under operating conditions, which can be overcome given that the explicit dynamics of the system is known. To provide a better understanding of the dynamic of Cu-Zn surface at the atomic level, the structure and the formation process of the Cu-Zn surface alloy on Cu(997) were investigated by machine-learning molecular dynamics (MD). Gaussian process regression aided with on-the-fly learning was employed to build the force field used in the MD. The simulation reveals atomistic details of the alloying process, that is, the incorporation of deposited Zn adatoms to the Cu substrate. The surface alloying is found to start at upper and lower terraces near the step edge, which emphasize the role of steps and kinks in the alloying. The incorporation of Zn at the middle terrace was found at the later stage of the simulation. The rationalization of alloying behavior was performed based on statistics and barriers of various elementary events that occur during the simulation. It was observed that the alloying scheme at the upper terrace is dominated by the confinement of Zn step adatoms by other adatoms, highlighting the importance of step fluctuations in the alloying process. On the other hand, the alloying scheme at the lower terrace is dominated by direct exchange between the Zn step adatom and the Cu atom underneath. The alloying at the middle terrace is dominated by the wave deposition mechanism and deep confinement of Zn adatoms. The short propagation of alloyed Zn in the middle terrace was observed to proceed by means of indirect exchange instead of local exchange as proposed in the previous scanning tunneling microscopy (STM) observation. The comparison of migration rate and activation energies to the result of STM observation is also made. We have found that at a certain distance from the surface, the STM tip significantly affects the elementary events such as vacancy formation and direct exchange.

Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity

The complex interaction between molecules and catalyst surfaces leads to great difficulties in understanding and predicting the activity and selectivity in heterogeneous catalysis. Here we develop an end-to-end artificial intelligence framework for the activity prediction of heterogeneous catalytic systems (AI-Cat method), which takes simple inputs from names of molecules and metal catalysts and outputs the reaction energy profile from the input molecule to low energy pathway products. The AI-Cat method combines two neural network models, one for predicting reaction patterns and the other for providing the reaction barrier and energy, with a Monte Carlo tree search to resolve the low energy pathways in a reaction network. We then apply AI-Cat to resolve the reaction network of glycerol hydrogenolysis on Cu surfaces, which is a typical selective C-O bond activation system and of key significance for biomass-derived polyol utilization. We show that glycerol hydrogenolysis features a huge reaction network of relevant candidates, containing 420 reaction intermediates and 2467 elementary reactions. Among them, the surface-mediated enol-keto tautomeric resonance is a key step to facilitate the primary C-OH bond breaking and thus selects 1,2-propanediol as the major product on Cu catalysts. 1,3-Propanediol can only be produced under strong acidic conditions and high surface H coverage by following a hydrogenation-dehydration pathway. AI-Cat further discovers six low-energy reaction patterns for C-O bond activation on metals that is of general significance to polyol catalysis. Our results demonstrate that the reaction prediction for complex heterogeneous catalysis is now feasible with AI-based atomic simulation and a Monte Carlo tree search.

Zeolite-confined subnanometric PtSn mimicking mortise-and-tenon joinery for catalytic propane dehydrogenation

Heterogeneous catalysts are often composite materials synthesized via several steps of chemical transformation, and thus the atomic structure in composite is a black-box. Herein with machine-learning-based atomic simulation we explore millions of structures for MFI zeolite encapsulated PtSn catalyst, demonstrating that the machine-learning enhanced large-scale potential energy surface scan offers a unique route to connect the thermodynamics and kinetics within catalysts' preparation procedure. The functionalities of the two stages in catalyst preparation are now clarified, namely, the oxidative clustering and the reductive transformation, which form separated Sn₄O₄ and PtSn alloy clusters in MFI. These confined clusters have high thermal stability at the intersection voids of MFI because of the formation of "Mortise-and-tenon Joinery". Among, the PtSn clusters with high Pt:Sn ratios (>1:1) are active for propane dehydrogenation to propene, ∼10³ in turnover-of-frequency greater than conventional Pt₃Sn metal. Key recipes to optimize zeolite-confined metal catalysts are predicted.

Machine learning potential era of zeolite simulation

Zeolites, owing to their great variety and complexity in structure and wide applications in chemistry, have long been the hot topic in chemical research. This perspective first presents a short retrospect of theoretical investigations on zeolites using the tools from classical force fields to quantum mechanics calculations and to the latest machine learning (ML) potential simulations. ML potentials as the next-generation technique for atomic simulation open new avenues to simulate and interpret zeolite systems and thus hold great promise for finally predicting the structure-functionality relation of zeolites. Recent advances using ML potentials are then summarized from two main aspects: the origin of zeolite stability and the mechanism of zeolite-related catalytic reactions. We also discussed the possible scenarios of ML potential application aiming to provide instantaneous and easy access of zeolite properties. These advanced applications could now be accomplished by combining cloud-computing-based techniques with ML potential-based atomic simulations. The future development of ML potentials for zeolites in the respects of improving the calculation accuracy, expanding the application scope and constructing the zeolite-related datasets is finally outlooked.

Automated search for optimal surface phases (ASOPs) in grand canonical ensemble powered by machine learning

The surface of a material often undergoes dramatic structure evolution under a chemical environment, which, in turn, helps determine the different properties of the material. Here, we develop a general-purpose method for the automated search of optimal surface phases (ASOPs) in the grand canonical ensemble, which is facilitated by the stochastic surface walking (SSW) global optimization based on global neural network (G-NN) potential. The ASOP simulation starts by enumerating a series of composition grids, then utilizes SSW-NN to explore the configuration and composition spaces of surface phases, and relies on the Monte Carlo scheme to focus on energetically favorable compositions. The method is applied to silver surface oxide formation under the catalytic ethene epoxidation conditions. The known phases of surface oxides on Ag(111) are reproduced, and new phases on Ag(100) are revealed, which exhibit novel structure features that could be critical for understanding ethene epoxidation. Our results demonstrate that the ASOP method provides an automated and efficient way for probing complex surface structures that are beneficial for designing new functional materials under working conditions.

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system.

GRAPHICAL ABSTRACT

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11244-021-01543-9.

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called operando modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.

Hydrogen Coupling on Platinum Using Artificial Neural Network Potentials and DFT

To date, the understanding of reactions at solid-liquid interfaces has proven challenging, mainly because of the inaccessible nature of such systems to current experimental techniques with atomic resolution. This has meant that many important features, including free energy barriers and the atomistic structure of intermediates, remain unknown. To tackle these issues, we construct and utilize a high-dimensional neural network (HDNN) potential for the simulation of hydrogen evolution at the HCl(aq)/Pt(111) interface, taking into consideration the influence of adsorbate-adsorbate, adsorbate-solvent interactions, and ion solvation explicitly. Long time scale MD simulations reveal coadsorbed H_ad/H₂O_ad on the surface. The free energy profiles for the Tafel and Heyrovsky type hydrogen coupling are extracted using umbrella sampling. It is found that the preferential mechanism can change depending on the surface coverage, highlighting the dual mechanistic nature for HER on Pt(111). Our work demonstrates the importance of controlling the solvent-substrate interactions in developing catalysts beyond Pt.

Shedding Light on the Role of Chemical Bond in Catalysis of Nitrogen Fixation

Ammonia (NH₃ ) and nitrates are essential for human society because of their widespread utilization for producing medicines, fibers, fertilizers, etc. In recent years, the development on nitrogen fixation under mild reaction conditions has attracted much attention. However, the very low conversion efficiency and ambiguous catalytic mechanism remain the major hurdles for the research of nitrogen fixation. This review aims to clarify the role of chemical bond in catalytic nitrogen fixation by summarizing and analyzing the recent development of nitrogen fixation research. In detail, the atomic-scale mechanism of nitrogen fixation reaction, the various methods to improve the nitrogen fixation performance, and the computational investigation of nitrogen fixation are discussed, all from a chemical bond perspective. It is hoped that this review could trigger more profound pondering and deeper exploration in the field of catalytic nitrogen fixation.

In Situ Surface Structures of PdAg Catalyst and Their Influence on Acetylene Semihydrogenation Revealed by Machine Learning and Experiment

PdAg alloy is an industrial catalyst for acetylene-selective hydrogenation in excess ethene. While significant efforts have been devoted to increase the selectivity, there has been little progress in the catalyst performance at low temperatures. Here by combining a machine-learning atomic simulation and catalysis experiment, we clarify the surface status of PdAg alloy catalyst under the reaction conditions and screen out a rutile-TiO₂ supported Pd₁Ag₃ catalyst with high performance: i.e., 85% selectivity at >96% acetylene conversion over a 100 h period in an experiment. The machine-learning global potential energy surface exploration determines the Pd-Ag-H bulk and surface phase diagrams under the reaction conditions, which reveals two key bulk compositions, Pd₁Ag₁ (R3̅m) and Pd₁Ag₃ (Pm3̅m), and quantifies the surface structures with varied Pd:Ag ratios under the reaction conditions. We show that the catalyst activity is controlled by the PdAg patterns on the (111) surface that are variable under reaction conditions, but the selectivity is largely determined by the amount of Pd exposure on the (100) surface. These insights provide the fundamental basis for the rational design of a better catalyst via three measures: (i) controlling the Pd:Ag ratio at 1:3, (ii) reducing the nanoparticle size to limit PdAg local patterns, (iii) searching for active supports to terminate the (100) facets.

The dome of gold nanolized for catalysis

Gold is noble in bulk but turns out to be a superior catalyst at the nanoscale when supported on oxides, in particular titania. The critical thickness for activity, namely two-layer gold particles on titania, observed two decades ago represents one of the most influential mysteries in the recent history of heterogeneous catalysis. By developing a Bayesian optimization controlled global potential energy surface exploration tool with machine learning potential, here we determine the atomic structures of gold particles within ∼2 nm on a TiO2 surface. We show that the smallest stable Au nanoparticle is Au24 which is pinned on the oxygen-rich TiO2 and exhibits an unprecedented dome architecture made by a single-layer Au sheet but with an apparent two-atomic-layer height. Importantly, this has the highest activity for CO oxidation at room temperature. The physical origin of the high activity is the outstanding electron storage ability of the nano-dome, which activates the lattice oxygen of the oxide. The determined CO oxidation mechanism, the simulated rate and the fitted apparent energy barrier are consistent with known experimental facts, providing key evidence for the presence of both the high-activity Au dome and the low-activity close-packed Au particles in real catalysts. The future direction for the preparation of active and stable Au-based catalysts is thus outlined.

Thermodynamic rules for zeolite formation from machine learning based global optimization

While the [TO4] tetrahedron packing rule leads to millions of likely zeolite structures, there are currently only 252 types of zeolite frameworks reported after decades of synthetic efforts. The subtle synthetic conditions, e.g. the structure-directing agents, pH and the feed ratio, were often blamed for the limited zeolite types due to the complex kinetics. Here by developing machine learning global optimization techniques, we are now able to establish the global potential energy surface of a typical zeolite system, Si x Al y P z O2H y-z with 12 T atoms (T: Si, Al and P) that is the general formula shared by CHA, ATS, ATO and ATV zeolite frameworks. After analyzing more than 106 minima data, we identify thermodynamic rules on energetics and local bonding patterns for stable zeolites. These rules provide general guidelines to classify zeolite types and correlate them with synthesis conditions. The machine learning based atomistic simulation thus paves a new way towards rational design and synthesis of stable zeolite frameworks with desirable compositions.

Ultrasmall Au clusters supported on pristine and defected CeO2: Structure and stability

The atomistic simulation of supported metal catalysts has long been challenging due to the increased complexity of dual components. In order to determine the metal/support interface, efficient theoretical tools to map out the potential energy surface (PES) are generally required. This work represents the first attempt to apply the recently developed SSW-NN method, stochastic surface walking (SSW) global optimization based on global neural network potential (G-NN), to explore the PES of a highly controversial supported metal catalyst, Au/CeO₂, system. By establishing the ternary Au-Ce-O G-NN potential based on first principles global dataset, we have searched for the global minima for a series of Au/CeO₂ systems. The segregation and diffusion pathway for Au clusters on CeO₂(111) are then explored by using enhanced molecular dynamics. Our results show that the ultrasmall cationic Au clusters, e.g., Au₄O₂, attaching to surface structural defects are the only stable structural pattern and the other clusters on different CeO₂ surfaces all have a strong energy preference to grow into a bulky Au metal. Despite the thermodynamics tendency of sintering, Au clusters on CeO₂ have a high kinetics barrier (>1.4 eV) in segregation and diffusion. The high thermodynamics stability of ultrasmall cationic Au clusters and the high kinetics stability for Au clusters on CeO₂ are thus the origin for the high activity of Au/CeO₂ catalysts in a range of low temperature catalytic reactions. We demonstrate that the global PES exploration is critical for understanding the morphology and kinetics of metal clusters on oxide support, which now can be realized via the SSW-NN method.