kMC Simulations Research Articles

Highly accelerated kinetic Monte Carlo models for depolymerisation systems

Kinetic Monte Carlo (kMC) models are a well-established modelling framework for the simulation of complex free-radical kinetic systems. kMC models offer the advantage of discretely monitoring every chain sequence in the system, providing full accounting of the chain molecular weight distribution. These models are marred by the necessity to simulate a minimum number of molecules, which confers significant computational burden. This paper adapts and creates a highly generalizable methodology for scaling dilute radical populations in discrete stochastic models, such as Gillespie's Stochastic Simulation Algorithm (SSA). The methodology is then applied to a kMC simulation of polystyrene (PS) pyrolysis, using a modelling framework adapted from literature. The results show that the required number of simulated molecules can be successfully reduced by up to three orders of magnitude with minimal loss of convergent behaviour, corresponding to a wall-clock simulation speed reduction of between 95.2 to 99.6 % at common pyrolysis temperatures.

Mechano-chemical competition in driven complex concentrated alloys

Mechanically driven complex concentrated alloys can be formed by three or more types of elemental powders mixing into a solid solution through severe plastic deformation at temperatures much lower than the melting point of each element. While competition between the thermal and mechanical driving forces during forced mixing of binary systems with positive enthalpy of mixing is relatively well understood, the physics of mechanically driven mixing of multiple elements with negative mixing enthalpies remained unclear. In this work, we combined mechanical alloying (MA), X-ray diffraction (XRD), and kinetic Monte Carlo (kMC) simulations to systematically study the interplay between the chemical pairing potential and the mechanical strength of elemental pairs during the formation process of mechanically driven complex concentrated alloys. We demonstrate that the chemical and mechanical forces play a competing role in the mixing of ternary complex concentrated alloys with negative mixing enthalpies. The chemical driving force favors a chemically ordered atomic structure, while the mechanical force encourages a random atomic arrangement. We reveal the energetic basis of this competition as the gain and loss in mixing enthalpy and configurational entropy. Following this fundamental understanding, three types of mixing mechanisms and their corresponding steady-state phases are defined. We show that the molar content of the element with the lowest average mixing enthalpy governs the mixing mechanisms and thus determines the energetic stabilization of the steady-state phases. A theoretical phase prediction map is provided for alloy design. We synthesized a nanocrystalline equiatomic NiCoCr coating under the guidelines of the map, which presents exceptional mechanical properties achieved by the mechano-chemical mixing.

Adsorption and evolution of N2 molecules over ZnO monolayer: a combined DFT and kinetic Monte-Carlo insight

A large surface area, wide band gap, and unique bonding property between Zn and O atoms make the hexagonal ZnO monolayer attractive as a gas sensor. In the present work, the adsorption and evolution of nitrogen (N2) molecules over a ZnO monolayer have been studied using two different theoretical methods: van der Waals density functional theory (vdW-DFT) and kinetic Monte-Carlo (kMC) simulation. The adsorption and diffusion (hopping over the surface) energy of a N2 gas molecule has been calculated considering the different sites over the ZnO substrate using the revPBE-vdW functional. Bader charge, electron localization function analysis, density of states and band structure plotting have been used to understand the adsorption mechanism. Lateral repulsive interaction between two N2 molecules limits the maximum packing number of gas molecules within one hexagonal ring. The output of the vdW-DFT calculation has been fed to the kMC code to predict the rate of adsorption, desorption, and diffusion, along with the overall surface coverage at different temperatures and pressures. Finally, the change in the N2 adsorption energy has been predicted with the increase of the ZnO layer number.

Machine learning-augmented modeling on the formation of Si-dominated Non-β″ early-stage precipitates in Al-Si-Mg alloys with Si supersaturation induced by non-equilibrium solidification

Recent experiments have shown that Al-Si-Mg alloys solidified under high cooling rates may lead to the nucleation of Si-enriched clusters that are remarkably different from the conventional Mg-Si co-clusters (e.g. β″ particles), and yet the responsible mechanism remains to be elucidated. Here we tackle the problem using a multiscale modeling framework that integrates atomistic modeling, energy landscape sampling, and lattice-based kinetic Monte Carlo (kMC) simulation. The migration energy barriers for vacancy-mediated diffusion amid complex local chemical environments are predicted on-the-fly using a surrogate machine learning model. We discover that the actual vacancy-Si migration barriers are much lower than those assumed in the classic linear interpolation approximation. Such a strong deviation from conventional wisdom, in conjunction with differing Si solute composition, can lead to a great variety in the nucleated early-stage precipitates. More specifically, a high-level supersaturation of Si solute (i.e.xSi/(xSi+xMg)>0.75) would lead to an unexpectedly high enrichment of Si in the nucleated clusters with the Si:Mg ratio up to 5∼6; while at a lower-level supply of Si solute the Mg-Si co-clusters (i.e. Si:Mg ratio around 1∼2) are nucleated instead. These findings provide a viable explanation for the diverse types of early-stage precipitates observed in various experiments, from Si-enriched precipitates in high-pressure die cast Al alloys to β″ particles in conventional casting and/or heat-treated alloys. The implications of our findings are also discussed.

Depressurization-induced production of shale gas in organic-inorganic shale nanopores: A kinetic Monte Carlo simulation

Methane gas production from unconventional reservoirs, such as shale formations, is in high demand due to the global energy needs. However, maximizing production remains challenging due to the ultra-tight porosity, heterogeneity, and complex nature of shale rocks under high pressure. To address this issue, we employ a novel kinetic Monte Carlo (kMC) simulation to investigate the molecular-level behavior of shale gas in both homogeneous and heterogeneous organic-inorganic shale nanopores. This approach not only provides accurate computations of shale gas under high pressure but also aims to uncover the mechanisms of shale gas storage at reservoir pressure and production during pressure drawdown. Our findings indicate that organic shale ultramicropores (pore width < 0.7 nm) contribute significantly to the highest storage capacity of shale gas but pose challenges for production capacity solely through depressurization. In contrast, the free gas zone is the primary source of shale gas production from mesoporous shales, which has a high recovery efficiency but a low production capacity due to less pronounced interaction effects from the shale surface. The heterogeneous nature of shale surfaces leads to asymmetric distributions of density and potential energy across pore widths, with methane molecules favoring locations near organic pore walls due to stronger attractive interactions, while inorganic pore walls facilitate shale gas migration. Interestingly, the optimal ultramicropore size yields the highest recovery efficiency of shale gas via depressurization, characterized by a transition from near-commensurate to incommensurate molecular packing between reservoir and post-drawdown pressures. Based on these detailed molecular simulations, further research is necessary to develop innovative techniques for enhancing shale gas recovery, especially in micropores.

Multiscale modeling of catalyst deactivation in dry methane reforming

Dry methane reforming (DRM) presents a promising strategy to convert greenhouse gases (CH4 and CO2) into syngas, a valuable precursor for the production of long-chain alkanes. However, catalyst deactivation remains a major issue, primarily caused by the deep cracking of CH4 and CO2 as well as the metal-catalyzed growth of carbon whiskers. To address this challenge, we introduce a multiscale model that analyzes DRM reactions on the Ni surface and predicts whisker formation. The multiscale modeling approach integrates microscopic kinetic Monte Carlo (kMC) simulations for surface reactions, mesoscopic pellet-scale modeling for predicting whisker growth length and macroscopic modeling to consider packed bed porosity and pressure drop across the reactor. Further, by systematically introducing time-steppers using the gap-tooth scheme, we significantly improved the computational efficiency, reducing the computational time from 17 days to 6 h at the expense of minimal accuracy loss in predicting whisker length. Based on this, we assert that such a multiscale model enables the analysis of various operating conditions affecting catalyst deactivation and kinetics of surface reaction, aiding in the design and optimization of DRM process.

Versatile stochastic model for predictive KMC simulation of fcc metal nanostructure evolution with realistic kinetics.

Stochastic lattice-gas models provide the natural framework for analysis of the surface diffusion-mediated evolution of crystalline metal nanostructures on the appropriate time scale (often 101-104s) and length scale. Model behavior can be precisely assessed by kinetic Monte Carlo simulation, typically incorporating a rejection-free algorithm to efficiently handle the broad range of Arrhenius rates for hopping of surface atoms. The model should realistically prescribe these rates, or the associated barriers, for a diversity of local surface environments. However, commonly used generic choices for barriers fail, even qualitatively, to simultaneously describe diffusion for different low-index facets, for terrace vs step edge diffusion, etc. We introduce an alternative Unconventional Interaction-Conventional Interaction formalism to prescribe these barriers, which, even with few parameters, can realistically capture most aspects of behavior. The model is illustrated for single-component fcc metal systems, mainly for the case of Ag. It is quite versatile and can be applied to describe both the post-deposition evolution of 2D nanostructures in homoepitaxial thin films (e.g., reshaping and coalescence of 2D islands) and the post-synthesis evolution of 3D nanocrystals (e.g., reshaping of nanocrystals synthesized with various faceted non-equilibrium shapes back to 3D equilibrium Wulff shapes).

Real-Time KMC Simulation of Vacancy-Mediated Intermixing in Au@Ag Octahedral Core-Cubic Shell Nanocrystals with Ab Initio-Guided Kinetics.

Utilization of core-shell rather than monometallic nanocrystals (NCs) facilitates fine-tuning of NC properties for applications. However, compositional evolution via intermixing can degrade these properties prompting recent experimental studies. We develop an atomistic-level stochastic model for vacancy-mediated intermixing exploiting a formalism which allows incorporation at an ab initio density functional theory level of not just the thermodynamics of vacancy formation, but also relevant diffusion barriers for a vast number of possible local environments (in the core and in the shell, at the interface, and in the intermixed phase). This facilitates a predictive treatment and comprehensive understanding of intermixing on the relevant time scale (e.g., 101-103 s). In contrast, previous modeling at the atomistic level utilized only unrealistic generic prescriptions of barriers or employed simplified continuum treatments. For Au@Ag octahedral core-cubic shell NCs, our modeling not only captures the experimentally observed rate or time scale for intermixing of ∼100 s at 450 °C for 60 nm NCs, but also elucidates the underlying rate controlling processes and the effective intermixing barrier.

Comparative simulations of methanol steam reforming on PdZn alloy using kinetic Monte Carlo and mean-field microkinetic model.

Methanol steam reforming (MSR) is an attractive route for producing clean energy hydrogen. PdZn alloys are extensively studied as potential MSR catalysts for their stability and high CO2 selectivity. Here, we investigated the reaction mechanism using density functional calculations, mean-field microkinetic modeling (MF-MKM), and kinetic Monte Carlo (kMC) simulations. To overcome the over-underestimation of CO2 selectivity by log-kMC, an ads-kMC algorithm is proposed in which the adsorption/desorption rate constants were reduced under certain requirements and the diffusion process was treated by redistributing surface species each time an event occured. The simulations show that the dominant pathway to CO2 at low temperatures is CH3OH → CH3O → CH2O → H2COOH → H2COO → HCOO → CO2. The ads-kMC predicted OH coverage is 2-3 times that of MF-MKM, while they produce similar coverage for other species. Analyses indicate that surface OH promotes the dehydrogenation of CH3OH, CH3O, and H2COOH significantly and plays a key role in the MSR process. The dissociation of water/methanol is the most important rate-limiting/rate-inhibiting step. The CO2 selectivity obtained by the two methods is close to each other and consistent with the experimental trend with temperature. Generally, the ads-kMC results agree with the MF-MKM ones, supporting the previous finding that kMC and MF-MKM predict similar results if the diffusion is very fast and adsorbate interactions are neglected. The present study sheds light on the MSR process on PdZn alloys, and the proposed scheme to overcome the stiff problems in kMC simulations is worthy of being extended to other systems.

Theoretical study of the effects of surface Cu coordination environment on CO2 hydrogenation to CH3OH

The coordination environment of Cu (the coordination number and arrangement of surface atoms) plays an important role in CO2 hydrogenation to CH3OH. Compared with the extensive studies of the effects of coordination number, the comprehensive effects of coordination number and arrangement of surface atoms were seldom explored in literature. To unravel the effects of surface Cu coordination environment on CO2 hydrogenation to CH3OH, the adsorption and reaction behaviors of H2 and CO2 on Cu(111), (100), (110) and (211) with different coordination numbers and arrangement of surface Cu were systematically calculated by density functional theory (DFT) and kinetic Monte Carlo (kMC) simulation. It was found that the adsorption energies of intermediates in CO2 hydrogenation on Cu surfaces increase with the decrease of coordination number. When the Cu coordination numbers are similar, the charge density on the open surface derived from the different atom arrangement becomes larger and leads to stronger interaction with intermediates than that on the compact one. DFT calculation and kMC simulation indicate that methanol formation pathway follows CO2*→HCOO*→HCOOH*→H2COOH*→H2CO*→CH3O*→CH3OH* on four Cu facets; CO formation is via CO2 direct dissociation on Cu(111), (100) and (110) but COOH* dissociation on (211). The low-coordinated surface Cu with more openness on Cu(211) is the highly active site for CO2 hydrogenation to CH3OH with high turnover of frequency (3.71 × 10−4 s−1) and high selectivity (87.17 %) at 600 K, PCO2 = 7.5 atm and PH2 = 22.5 atm, which is much higher than those on Cu(111), (100) and (110). This work unravels the effects of coordination environment on CO2 hydrogenation at the molecular level and provides an important insight into the design and development of catalysts with high performance in CO2 hydrogenation to CH3OH.

A simple model for short-range ordering kinetics in multi-principal element alloys

Short-range ordering (SRO) in multi-principal element alloys influences material properties such as strength and corrosion. While some degree of SRO is expected at equilibrium, predicting the kinetics of its formation is challenging. We present a simplified isothermal concentration-wave (CW) model to estimate an effective relaxation time of SRO formation. Estimates from the CW model agree to within a factor of five with relaxation times obtained from kinetic Monte Carlo (kMC) simulations when above the highest ordering instability temperature. The advantage of the CW model is that it only requires mobility and thermodynamic parameters, which are readily obtained from alloy mobility databases and Metropolis Monte Carlo simulations, respectively. The simple parameterization of the CW model and its analytical nature makes it an attractive tool for the design of processing conditions to promote or suppress SRO in multicomponent alloys.

The types of metal M and crystal surface in the PtM (M = Co, Ni, Zn) IMCs catalysts to regulate catalytic performance of ethane direct dehydrogenation

PtM IMCs have improved catalytic performance of ethane dehydrogenation (EDH), however, identifying the functions of the types of metal M and crystal surface for the PtM IMCs catalysts in regulating EDH catalytic performance are still a challenge in experiment. This work fully investigated catalytic performance of EDH over three crystal surfaces (110), (111) and (200) of PtM IMCs (M = Co, Ni, Zn) by using density functional theory (DFT) calculations and kinetic Monte Carlo (kMC) simulations. The results show that the catalytic performance of EDH over PtM IMCs strongly depend on the types of metal M and crystal surfaces, the PtZn(111) catalyst is screened out as the most promising in EDH reaction with the optimal reaction temperature of 773.15 K. The geometrically isolated Pt site in the [PtM3] assembly of PtZn(111) could catalyze the CH cleavage, and C2H4* exhibits weak π-adsorption model at the individual Pt sites, promoting C2H4* desorption. Compared to (110) and (200) surfaces, the (111) surface with moderate Bader charge transfer showed the highest C2H4(g) production activity, and Zn atoms transferred more electrons to Pt atoms, which greatly weakened the charge transfer between C2H4* and the catalyst surface, favoring C2H4* desorption to inhibit C2H4* deep dehydrogenation. Moreover, the catalytic performance of PtZn(111) catalyst is superior to the industrially used PtSn catalyst. This work provides valuable structure clue for the design of high-performance catalyst in alkane direct dehydrogenation.

Unraveling the function of monolayer shell structure over Pt-based alloy catalysts in tuning ethane dehydrogenation reactivity and coking resistance

Ethane direct dehydrogenation (EDH) operated at high temperature easily leads to coke formation. The new core–shell catalysts with monolayer shell exhibited better catalytic performance, and the function of monolayer shell structure is of great importance. This work fully investigated catalytic performance of EDH over six types of core–shell Pt3M@Pt and Pt@Pt3M (M = Fe, Co, and Ni) catalysts with Pt3M and Pt monolayer shell under the experimental conditions. Here, density functional theory (DFT) calculations with kinetic Monte Carlo (kMC) simulations were employed. The results show that the metal M over Pt3M@Pt surface decreases coordination number of Pt-Pt and restricts deep dehydrogenation reactions. The Pt3M@Pt catalysts with d-band center close to Fermi level exhibit higher C2H4(g) formation activity than Pt@Pt3M catalysts at 873.15 K. H2(g) co-feeding significantly decreases the coverage of coking C*+CC* species. The screened Pt3Ni@Pt catalyst presents the highest C2H4(g) formation activity and 100 % selectivity at the H2(g) partial pressure of 0.1 bar and reaction temperature 873.15 K, which is superior to previously reported Pt, Pt3Sn, 1Pt3Sn@4Pt, Pd1-NDG, Pt2/Al2O3 and Rh/ZrO2 catalysts. H* adsorption energy is proposed as a descriptor to quickly evaluate C2H4(g) formation activity. This work provides a useful strategy for designing alkane dehydrogenation catalysts by tuning monolayer shell structure and coordination environment of active site.

Understanding Substrate Effects on 2D MoS2 Growth: A Kinetic Monte Carlo Approach

AbstractControlling the morphology of 2D transition metal dichalcogenides (TMDs) plays a key role in their applications. Although chemical vapor deposition can achieve wafer‐scale growth of 2D TMDs, a comprehensive theoretical framework for effective growth optimization is lacking. Atomistic modeling methods offer a promising approach to delve into the intricate dynamics underlying the growth. In this study, kinetic Monte Carlo (kMC) simulations are employed to identify crucial parameters that govern the morphology of MoS2 flakes grown on diverse substrates. The simulations reveal that large adsorption rates significantly enhance growth speed, which however necessitates rapid edge migration to achieve compact triangles. Substrate etching can tune the adsorption–desorption process of adatoms and enable preferential growth within a specific substrate region, controlling the flake morphology. This study unravels the complex dynamics governing 2D TMD morphology, offering a theoretical framework for decision‐making in the design and optimization of TMD synthesis processes.

Metal nanowires grown on Cu (100) substrate

We present the growth mechanisms of Ag, Ni, Pd, Co and Fe nanowires on Cu(100) substrate studied by the KMC simulations. It has been allows to found the optimum growth parameters (T, F, θ). Their effects have been discussed in details. The quality of nanowires has been determined by calculating the filling ratio. Our simulations exhibit that the best quality is obtained for Ag NWs. In fact, the maximum filling ratio of Ag NWs is reached for temperature range between 200 and 500 K. The increasing of the deposition flux makes decreases the filling ratio. We get an inverse behavior when the coverage is increased for two different temperatures.

Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization.

Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymers with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to achieve the targeted polymer property. The objectives for optimization include the maximal similarity of molar mass distributions (MMDs) compared to the target MMDs, a minimal reaction time, and maximal monomer conversion. The method is tested for vinyl acetate radical polymerizations and can be adopted to other monomers. The data for the optimization procedure are generated by an in-house-developed kinetic Monte-Carlo (kMC) simulator for a selected recipe search space. The proposed reverse engineering algorithm comprises several steps: kMC simulations for the selected recipe search space to derive initial data, performing MOO for a targeted MMD, and the identification of the Pareto optimal space. The last step uses a weighted sum optimization function to calculate the weighted score of each candidate polymerization condition. To decrease the execution time, clustering of the search space based on MMDs is applied. The performance of the proposed approach is tested for various target MMDs. The suggested MOO-based reverse engineering provides multiple recipe candidates depending on competing objectives.

Multiscale Investigation of the Mechanism and Selectivity of CO2 Hydrogenation over Rh(111).

CO2 hydrogenation over Rh catalysts comprises multiple reaction pathways, presenting a wide range of possible intermediates and end products, with selectivity toward either CO or methane being of particular interest. We investigate in detail the reaction mechanism of CO2 hydrogenation to the single-carbon (C1) products on the Rh(111) facet by performing periodic density functional theory (DFT) calculations and kinetic Monte Carlo (kMC) simulations, which account for the adsorbate interactions through a cluster expansion approach. We observe that Rh readily facilitates the dissociation of hydrogen, thus contributing to the subsequent hydrogenation processes. The reverse water-gas shift (RWGS) reaction occurs via three different reaction pathways, with CO hydrogenation to the COH intermediate being a key step for CO2 methanation. The effects of temperature, pressure, and the composition ratio of the gas reactant feed are considered. Temperature plays a pivotal role in determining the surface coverage and adsorbate composition, with competitive adsorption between CO and H species influencing the product distribution. The observed adlayer configurations indicate that the adsorbed CO species are separated by adsorbed H atoms, with a high ratio of H to CO coverage on the Rh(111) surface being essential to promote CO2 methanation.

Improved Approach for ab Initio Calculations of Rate Coefficients for Secondary Reactions in Acrylate Free-Radical Polymerization.

Secondary reactions in radical polymerization pose a challenge when creating kinetic models for predicting polymer structures. Despite the high impact of these reactions in the polymer structure, their effects are difficult to isolate and measure to produce kinetic data. To this end, we used solvation-corrected M06-2X/6-311+G(d,p) ab initio calculations to predict a complete and consistent data set of intrinsic rate coefficients of the secondary reactions in acrylate radical polymerization, including backbiting, β-scission, radical migration, macromonomer propagation, mid-chain radical propagation, chain transfer to monomer and chain transfer to polymer. Two new approaches towards computationally predicting rate coefficients for secondary reactions are proposed: (i) explicit accounting for all possible enantiomers for reactions involving optically active centers; (ii) imposing reduced flexibility if the reaction center is in the middle of the polymer chain. The accuracy and reliability of the ab initio predictions were benchmarked against experimental data via kinetic Monte Carlo simulations under three sufficiently different experimental conditions: a high-frequency modulated polymerization process in the transient regime, a low-frequency modulated process in the sliding regime at both low and high temperatures and a degradation process in the absence of free monomers. The complete and consistent ab initio data set compiled in this work predicts a good agreement when benchmarked via kMC simulations against experimental data, which is a technique never used before for computational chemistry. The simulation results show that these two newly proposed approaches are promising for bridging the gap between experimental and computational chemistry methods in polymer reaction engineering.

Adsorption and evolution of hydrogen molecules on hexagonal boron nitride monolayer: a combined DFT and kinetic monte-carlo simulations study

Density functional theory (DFT) and kinetic Monte-Carlo (kMC) simulation code has been combinedly used to study the adsorption and evolution dynamics of hydrogen molecules over a hexagonal boron nitride (h-BN) monolayer. Maximum adsorption energy from van der Waals curve is predicted to be around 60 to 70 meV using two different DFT functionals. Repulsive lateral interaction between two hydrogen molecules plays a key role in determining the maximum number of adsorptions inside one unit cell of h-BN. Bader charge analysis, electron localization function (ELF), total and partial density of states (DOS) plots have been included to understand the weak interaction going on between the adsorbent and substrate. The input energy parameters from the DFT calculation has been used to perform the kMC simulation for describing the adsorption, desorption and the diffusion pattern of hydrogen molecules with a given time of exposure to an empty h-BN substrate along with the overall surface coverage.

Kinetic Monte Carlo simulations of solute clustering during quenching and aging of Al–Mg–Zn alloys

The physical mechanisms behind cluster formation during quenching and aging of age-hardening metallic alloys are poorly understood based on classical nucleation and growth theories, especially in multicomponent alloys with supersaturated vacancies and fast-diffusing solute atoms. Here, solute clustering in an Al–Mg–Zn alloy during quenching immediately after high-temperature (800 K) solution treatment is modeled with a newly developed kinetic Monte Carlo (kMC) framework. This includes accurate surrogate models trained using first-principles-calculated data to predict vacancy migration energetics on the fly as functions of local lattice occupations. First, kMC simulations at constant aging temperatures revealed that temporal evolution of the number, sizes, and compositions of solute clusters change with aging temperature. Such changes are consistent with our analyses based on classical nucleation theory (CNT) and Monte Carlo simulations. Second, the kMC algorithm was revised for simulating quenching processes by iteratively updating the kMC temperature based on simulated cooling rate profiles. These quenching simulations show that rapid solute clustering mainly occurs from ∼600 to ∼400 K during cooling. Below ∼400 K, the clustering is suppressed to a steady state because vacancies are trapped by stable clusters with sizes of ∼2 nm and high magnitudes of formation energies. The configurations of these steady-state clusters closely resemble the solute clusters we observed experimentally in 7XXX series Al–Mg–Zn-based alloy samples immediately after quenching. A two-stage vacancy trapping mechanism revealed in our simulations can provide physical guidance to tune precipitation kinetics during natural and artificial aging.