kMC Simulations Research Articles

Accelerated kinetic Monte Carlo: A case study; vacancy and dumbbell interstitial diffusion traps in concentrated solid solution alloys.

Vacancy and self-interstitial atomic diffusion coefficients in concentrated solid solution alloys can have a non-monotonic concentration dependence. Here, the kinetics of monovacancies and ⟨100⟩ dumbbell interstitials in Ni-Fe alloys are assessed using lattice kinetic Monte Carlo (kMC). The non-monotonicity is associated with superbasins, which impels using accelerated kMC methods. Detailed implementation prescriptions for first passage time analysis kMC (FPTA-kMC), mean rate method kMC (MRM-kMC), and accelerated superbasin kMC (AS-kMC) are given. The accelerated methods are benchmarked in the context of diffusion coefficient calculations. The benchmarks indicate that MRM-kMC underestimates diffusion coefficients, while AS-kMC overestimates them. In this application, MRM-kMC and AS-kMC are computationally more efficient than the more accurate FPTA-kMC. Our calculations indicate that composition dependence of migration energies is at the origin of the vacancy's non-monotonic behavior. In contrast, the difference between formation energies of Ni-Ni, Ni-Fe, and Fe-Fe dumbbell interstitials is at the origin of their non-monotonic diffusion behavior. Additionally, the migration barrier crossover composition-based on the situation where Ni or Fe atom jumps have lower energy barrier than the other one-is introduced. KMC simulations indicate that the interplay between composition dependent crossover of migration energy and geometrical site percolation explains the non-monotonic concentration-dependence of atomic diffusion coefficients.

Rapid evaluation method for anisotropic growth of WS2 monolayers by combining machine learning algorithms and kinetic Monte Carlo simulation data

The growth of two-dimensional (2D) materials such as transition metal dichalcogenide (TMDC) monolayers is challenging and their growth mechanism has not been fully understood. It requires a large number of growth experiments and simulation calculations to reveal the growth mechanism. Also, a computational framework for combining the simulation results and the experimental results is essential. To speed up this time-consuming process for understanding the growth mechanism, a preliminary framework based on machine learning is herein proposed to analyze the simulation data and predict the growth trend. Specifically, the framework can be used for studying the anisotropic growth of tungsten disulfide (WS2) monolayers based on the data of kinetic Monte Carlo (kMC) simulations. Both an isotropic model with 91% accuracy and an anisotropic model with 90% accuracy are obtained. Moreover, both an undergrowth model with 98% accuracy and an overgrowth model with 99% accuracy are also obtained. This proposed framework can take both the experimental data and the simulation data as a single input data set, much speeding up the study of the growth mechanism.

Efficient and exact sampling of transition path ensembles on Markovian networks.

The problem of flickering trajectories in standard kinetic Monte Carlo (kMC) simulations prohibits sampling of the transition path ensembles (TPEs) on Markovian networks representing many slow dynamical processes of interest. In the present contribution, we overcome this problem using knowledge of the metastable macrostates, determined by an unsupervised community detection algorithm, to perform enhanced sampling kMC simulations. We implement two accelerated kMC methods to simulate the nonequilibrium stochastic dynamics on arbitrary Markovian networks, namely, weighted ensemble (WE) sampling and kinetic path sampling (kPS). WE-kMC utilizes resampling in pathway space to maintain an ensemble of representative trajectories covering the state space, and kPS utilizes graph transformation to simplify the description of an escape trajectory from a trapping energy basin. Both methods sample individual trajectories governed by the linear master equation with the correct statistical frequency. We demonstrate that they allow for efficient estimation of the time-dependent occupation probability distributions for the metastable macrostates, and of TPE statistics, such as committor functions and first passage time distributions. kPS is particularly attractive, since its efficiency is essentially independent of the degree of metastability, and we suggest how the algorithm could be coupled with other enhanced sampling methodologies. We illustrate our approach with results for a network representing the folding transition of a tryptophan zipper peptide, which exhibits a separation of characteristic timescales. We highlight some salient features of the dynamics, most notably, strong deviations from two-state behavior, and the existence of multiple competing mechanisms.

Interconversion of Lewis acid and Brønsted acid catalysts in biomass-derived paraxylene synthesis

The change of acidity of catalyst site during the catalytic reaction may enforce great influence on the efficiency of catalyst. Here, the WOx catalyst highly dispersed on surface of SiO2 molecular sieve is synthesized, which is found to possess strong Lewis acidity and great catalytic ability for producing paraxylene through the Diels-Alder cycloaddition reaction of 2,5-dimethylfuran and ethylene. Based on a comprehensive density functional theory study, we find that the water generated during the reaction can convert the Lewis acid sites into Brønsted acid sites, thereby, turn the Lewis acid catalytic reaction into the Brønsted acid assisted reaction. The kinetic Monte Carlo (kMC) simulations reveal that the selectivity of PX relative to the by-product 2,5-hexanedione increases with reaction temperature, which is consistent with the experimental observation. The unravelling of the interconversion of Lewis acid and Brønsted acid gives us new insight of the mechanism of acid-catalytic reactions.

Ghost‐Mirror Approach for Accurate and Efficient Kinetic Monte Carlo Simulation of Seeded Emulsion Polymerization

AbstractThe kinetic Monte Carlo (kMC) method is well suited to the simulation of seeded emulsion polymerization reactions. Inadequate simulation volume results in an incorrect radical entry rate, and as such that inaccuracy propagates into the particle phase simulation. A novel kMC method is described here that has coined the Ghost‐Mirror approach (GM‐kMC) to guarantee sufficient aqueous phase simulation volume while minimizing the number of dispersed phase environments, and while maintaining accuracy and consistency with results produced by kMC simulations. To do so, a subset of a kMC method is replicated where only the aqueous phase reactions are treated in that extended volume; particle phases in those replicated volumes are untreated, as “ghosts”. In doing so, a critical condition for aqueous phase simulation volume is calculated to yield accurate results in the overall system. Important to emphasize though is that one does not require the equivalent number of simulated particles in the GM‐kMC approaches. The required number of particles to simulate accurate results can be even orders of magnitude smaller than the kMC approach; inherently offering significant gains in computational efficiency. This is extended further, reducing the required simulation time, by a hybrid extension to this GM‐kMC method.

Multiscale modeling of a free-radical emulsion polymerization process: Numerical approximation by the Finite Element Method

A multiscale modeling description of free-radical polymerization processes is presented. The polymerization process is described at the macroscale by coupling the Fokker-Planck Equation (FPE) for the particle size distribution (PSD) prediction at the mesoscale with a kinetic Monte Carlo (kMC) simulation at the microscale. The finite element method is adopted to solve the mesoscopic scale to capture the nonlinear evolution of the PSD, successfully facing challenges related to accuracy and computational cost in the FPE numerical solution. Additionally, the proposed model captures the evolution of the average number of free-radicals and secondary nucleation rate at the microscopic level. The control of the secondary nucleation rate is in fact critical to satisfactorily obtain high quality structured polymer particles. Finally, a closed-form model is developed at the microscopic scale to handle the curse of dimensionality. Simulations to evaluate the capabilities of the proposed numerical scheme and sensitivity analyses with respect to the system inputs and uncertainties in the initial condition of the PSD are performed.

Morphology evolution and dendrite growth in Li- and Mg-metal batteries: A potential dependent thermodynamic and kinetic multiscale ab initio study

It is well accepted that dendrites grow on anode surface of lithium-metal based batteries, while magnesium-metal batteries do not exhibit such a behavior. However, it has been recently shown experimentally and theoretically that magnesium can form uneven deposits that lead to similar safety concerns as lithium dendrites. To investigate a complex phenomenon such as dendritic growth from a theoretical point of view, it is necessary to study it on a multiscale level, i.e. from atomistic to meso scale. Herein we investigate thermodynamics, kinetics and morphology evolution of magnesium and lithium surfaces using density functional theory (DFT) and kinetic Monte Carlo (kMC) simulations. To be realistic for a device such as a battery, the electrochemical dimension has also to be included: this study was done within the framework of Grand canonical DFT approach that enables potential dependent DFT calculations, simulating relevant battery operating conditions. The atomistic potential dependent DFT results were upscaled using kMC simulations allowing to extract mesoscopic behavior at various operating potentials. The results of these calculations demonstrate the effect of potential on both the energetics and the dynamics of diffusion at the electrode-electrolyte interface. In particular, the equilibrium morphology of the metal electrode is strongly modified by the potential, leading to the stabilization of different surface orientations with potential. We show that the applied potential impacts atomic diffusion barriers leading to different surface mesoscopic relaxation times in the overall kinetics. The obtained results provide not only potential dependent information about systems morphology evolution, but also demonstrate the importance of using a suitable potential range (i.e. coherent with the experimental conditions): an unrealistic potential could lead to a very different behavior of the system on atomic and on meso scale.

An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics

Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this work, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

Multiscale Analysis of Phase Transformations in Self-Assembled Layers of 4,4'-Biphenyl Dicarboxylic Acid on the Ag(001) Surface.

Understanding the nucleation and growth kinetics of thin films is a prerequisite for their large-scale utilization in devices. For self-assembled molecular phases near thermodynamic equilibrium the nucleation-growth kinetic models are still not developed. Here, we employ real-time low-energy electron microscopy (LEEM) to visualize a phase transformation induced by the carboxylation of 4,4'-biphenyl dicarboxylic acid on Ag(001) under ultra-high-vacuum conditions. The initial (α) and transformed (β) molecular phases are characterized in detail by X-ray photoemission spectroscopy, single-domain low-energy electron diffraction, room-temperature scanning tunneling microscopy, noncontact atomic force microscopy, and density functional theory calculations. The phase transformation is shown to exhibit a rich variety of phenomena, including Ostwald ripening of the α domains, burst nucleation of the β domains outside the α phase, remote dissolution of the α domains by nearby β domains, and a structural change from disorder to order. We show that all phenomena are well described by a general growth-conversion-growth (GCG) model. Here, the two-dimensional gas of admolecules has a dual role: it mediates mass transport between the molecular islands and hosts a slow deprotonation reaction. Further, we conclude that burst nucleation is consistent with a combination of rather weak intermolecular bonding and the onset of an additional weak many-body attractive interaction when a molecule is surrounded by its nearest neighbors. In addition, we conclude that Ostwald ripening and remote dissolution are essentially the same phenomenon, where a more stable structure grows at the expense of a kinetically formed, less stable entity via transport through the 2D gas. The proposed GCG model is validated through kinetic Monte Carlo (kMC) simulations.

Acceleration scheme for particle transport in kinetic Monte Carlo methods.

Kinetic Monte Carlo (kMC) simulations are frequently used to study (electro-)chemical processes within science and engineering. kMC methods provide insight into the interplay of stochastic processes and can link atomistic material properties with macroscopic characteristics. Significant problems concerning the computational demand arise if processes with large time disparities are competing. Acceleration algorithms are required to make slow processes accessible. Especially, the accelerated superbasin kMC (AS-kMC) scheme has been frequently applied within chemical reaction networks. For larger systems, the computational overhead of the AS-kMC is significant as the computation of the superbasins is done during runtime and comes with the need for large databases. Here, we propose a novel acceleration scheme for diffusion and transport processes within kMC simulations. Critical superbasins are detected during the system initialization. Scaling factors for the critical rates within the superbasins, as well as a lower bound for the number of sightings, are derived. Our algorithm exceeds the AS-kMC in the required simulation time, which we demonstrate with a 1D-chain example. In addition, we apply the acceleration scheme to study the time-of-flight (TOF) of charge carriers within organic semiconductors. In this material class, time disparities arise due to a significant spread of transition rates. The acceleration scheme allows a significant acceleration up to a factor of 65 while keeping the error of the TOF values negligible. The computational overhead is negligible, as all superbasins only need to be computed once.

Si-Doped Fe Catalyst for Ammonia Synthesis at Dramatically Decreased Pressures and Temperatures.

The Haber-Bosch (HB) process combining nitrogen (N2) and hydrogen (H2) into ammonia (NH3) gas plays an essential role in the synthesis of fertilizers for food production and many other commodities. However, HB requires enormous energy resources (2% of world energy production), and the high pressures and temperatures make NH3 production facilities very expensive. Recent advances in improving HB catalysts have been incremental and slow. To accelerate the development of improved HB catalysts, we developed a hierarchical high-throughput catalyst screening (HHTCS) approach based on the recently developed complete reaction mechanism to identify non-transition-metal (NTM) elements from a total set of 18 candidates that can significantly improve the efficiency of the most active Fe surface, Fe-bcc(111), through surface and subsurface doping. Surprisingly, we found a very promising subsurface dopant, Si, that had not been identified or suggested previously, showing the importance of the subsurface Fe atoms in N2 reduction reactions. Then we derived the full reaction path of the HB process for the Si doped Fe-bcc(111) from QM simulations, which we combined with kinetic Monte Carlo (kMC) simulations to predict a ∼13-fold increase in turnover frequency (TOF) under typical extreme HB conditions (200 atm reactant pressure and 500 °C) and a ∼43-fold increase in TOF under ideal HB conditions (20 atm reactant pressure and 400 °C) for the Si-doped Fe catalyst, in comparison to pure Fe catalyst. Importantly, the Si-doped Fe catalyst can achieve the same TOF of pure Fe at 200 atm/500 °C under much milder conditions, e.g. at a much decreased reactant pressure of 20 atm at 500 °C, or alternatively at temperature and reactant pressure decreased to 400 °C and 60 atm, respectively. Production plants using the new catalysts that operate under such milder conditions could be much less expensive, allowing production at local sites needing fertilizer.

Towards understanding the role of carbon atoms on transition metal surfaces: Implications for catalysis

Carbon moieties, in a low coverage regime being reduced to C adatoms, are a rock-in-the-shoe for heterogeneously catalyzed processes involving carbon-containing species. Their presence affects the performance of Transition Metal (TM) based industrial catalysts, often resulting in poisoning. Recent studies on the C adatom thermodynamic stability revealed that both surface and subsurface C atoms may coexist, indicating additional poisoning mechanisms, yet also new catalytic promoting mechanisms. The present work provides a systematic study of the potential dynamic relevance of such subsurface C atoms in the most stable (111) surface of all fcc TMs at low C coverages. This relies on evaluating the composition at thermodynamic equilibrium and the time scale of the different involved processes by means of Density Functional Theory (DFT) and kinetic Monte Carlo (kMC) simulations, respectively. These DFT and kMC simulations highlight the relevant role of subsurface C atoms for Ag and Pd, and a fast C mobility for Au and Pt, which might be important factors contributing to poisoning or opening new reactive path mechanisms, especially relevant at high temperature working conditions.

Adsorption of CO and desorption of CO2 interacting with Pt (111) surface: a combined density functional theory and Kinetic Monte Carlo simulation

The adsorption of CO and desorption of CO2 interacting with the Pt (111) surface was investigated using Kinetic Monte Carlo (kMC) simulation. The processes involved an elementary oxidation/reduction reaction (ORR). In comparison with standard density functional theory (DFT), kMC can simulate systems at practical time scale since it is concerned with the elementary reactions of the CO and O2 molecules adsorbed in the surface of the Pt system. The DFT results provide reliable estimates for adsorption and desorption energy barriers. The standard Arrhenius' equation serves as a working model to define the temperature dependence of individual elementary reaction rates (k). By incorporating the proper k obtained from DFT calculations into the kMC simulations, the study was able to reproduce acceptable result in agreement with practical microscopic reaction step occurring in the exhaust of gasoline engines. Thus, the kMC provides useful insight in the involved ORR steps in the interaction of CO and O2 with Pt. The ORR is sensitive to O–O dissociation compared with CO adsorption

A fast species redistribution approach to accelerate the kinetic Monte Carlo simulation for heterogeneous catalysis.

The first-principles kinetic Monte Carlo (kMC) simulation has been demonstrated as a reliable multiscale modeling approach in silico to disclose the interplay among all the elementary steps in a complex reaction network for heterogeneous catalysis. Heterogeneous catalytic systems frequently contain fast surface diffusion processes of some adsorbates while the elementary steps in it would be much slower than those in fast diffusion. Consequently, the kMC simulation for these systems is easily trapped in the sub-basins of a super basin on a potential energy surface due to the continuous and repeated sampling of these fast processes, which would significantly increase the total accessible simulation time and even make it impossible to get the reasonable simulation results using the kMC simulation. In this work, we present an improved fast species redistribution (FSR) method for the kMC simulation to overcome the stiffness problem resulting from the low-barrier surface diffusion to accelerate the heterogeneous catalytic kMC simulation. Taking CO oxidations on Pt(111) and Pt(100) as examples, we demonstrate that the FSR approach can properly reproduce the results of an equivalent first-principles microkinetic model simulation with more reasonable reaction rates. The improved kMC simulation based on the FSR method can accurately incorporate the effect of the fast diffusion of species on the surface and provide several orders of magnitude of acceleration compared to the standard kMC simulation.

Magnetic properties of the finite-length biatomic chains in the framework of the single domain-wall approximation

A simple analytical method for study the magnetic properties of the finite-length biatomic chains in the framework of Heisenberg model with uniaxial magnetic anisotropy is proposed. The method allows to estimate the reversal time of the magnetization of ferromagnetic and antiferromagnetic biatomic chains. Three cases are considered: the spontaneous remagnetization, the remagnetization under the interaction with a scanning tunneling microscope, and the remagnetization in the external magnetic field. The applicability limits of the method are discussed. Within its limits of applicability the method gives results which are in perfect agreement with the results of the kinetic Monte Carlo simulations. As the examples, two physical systems are considered: biatomic Fe chains on Cu2N/Cu(001) surface and biatomic Co chains on Pt(997) surface. The presented method is incomparably less time-consuming than the standard kMC simulations, especially in the cases of low temperatures or long chains.

CO Dissociation Mechanism on Mn-Doped Fe(100) Surface: A Computational Investigation

Periodic density function theory (DFT) and kinetic Monte Carlo (kMC) method are carried out to investigate CO dissociation process on the Mn-doped Fe(100) surface. The energetics information of relevant atomistic processes and adsorption features of relevant species are obtained from DFT calculations. Subsequently, kMC simulations are performed with DFT results employed as database. Simulations show that the energy barriers for CHO and COH formations are 0.09 eV and 0.35 eV larger than that for direct CO dissociation on Mn/Fe(100), respectively. An empty site is created with a CO hydrogenation (CO* + H* → COH* + *, CO* + H* → CHO* + *), while an active site is consumed with a CO direct dissociation (CO* + * → C* + O*). The number of unoccupied active sites can affect the way of CO dissociation. On surfaces with considerable unoccupied active sites, direct CO dissociation mechanism is the preferred route. Under conditions favoring a very low number of unoccupied active sites and a mass of adsorbed H on surfaces, H-assisted CO dissociation via COH will take place.

Kinetics of Anionic Living Copolymerization of Isoprene and Styrene Using in Situ NIR Spectroscopy: Temperature Effects on Monomer Sequence and Morphology

The living anionic copolymerization of isoprene (I) and styrene (S) can afford a variety of different polymer microstructures that strongly depend on experimental parameters such as solvent, counterion, and temperature. In this work, in situ near-infrared (NIR) spectroscopy was employed as a versatile and fast method to track the conversion of the individual monomers in a nonpolar (cyclohexane, CyH) and a coordinative solvent mixture (CyH with 1.5%vol tetrahydrofuran (THF)). For the first time, in situ monitoring of the copolymerization is performed by deriving the individual monomer signals from the superimposed spectra using the molar attenuation coefficients of each component in the wavenumber range of 5900–6250 cm–1. The polymerization in nonpolar solvents features a unique kinetic behavior and is known to result in tapered block copolymers. Kinetic rate constants and the corresponding activation energies were determined in CyH, covering a broad temperature range of 10–60 °C. Paralleling the experimental studies, the polymerization was also simulated in silico by feeding the measured kinetic rate constants into a kinetic Monte Carlo simulation (kMC). This combination of in situ monitoring and kMC simulation allows to reduce reaction times, which is especially desired in multiblock syntheses. The observed differences in activation energies aid in understanding the temperature dependence of the reactivity ratios. Thus, temperature can be used as an external parameter to adjust the gradient and the size of the polystyrene block. The peculiar temperature dependence of the gradient affects the resulting bulk morphology. This led to a surprising partial change from the lamellar to the tetragonally cylindrical or perforated lamellar structure at the identical isoprene/styrene composition, only caused by a change of the polymerization temperature. In situ NIR probing is established as a fast and accurate method for real-time copolymerization monitoring that enables tracking complex copolymerization procedures, such as multiblock formation with a temporal resolution exceeding current standards set by 1H NMR kinetics.

Carbon chain growth reaction of synthesis of lower olefins from syngas on Fe-Co catalyst

In recent years, the Fischer-Tropsch synthesis of lower olefins (FTO) has been an attractive non-oil-based process to produce lower olefins. Fe-based catalysts doped with Co promotor used for synthesis of lower olefins from syngas are studied in this work. We have used a combination of density functional theory (DFT) and kinetic Monte Carlo (kMC) simulations to explore the role of Co and Fe-Co catalytic activity in this reaction. DFT calculation results suggest that energy barriers of CHx coupling decrease significantly with the addition of Co while energy barriers of CHx hydrogenation decrease slightly, indicating that the addition of Co improves the ability of carbon chain growth. The results of kMC simulation show that the addition of Co increases reaction rates, and the carbon chain grows not only by CH2 coupling, but also by coupling of CH2 and CH3 in the presence of Co atoms. The appropriate Co content can increase the proportion of C2 hydrocarbons and C2H4 in the three main products. In summary, the appropriate addition of Co is beneficial to carbon chain growth, and the appropriate amount of Co can improve olefins selectivity effectively, which provides a theoretical basis for the preparation of Fe-Co catalyst for synthesis of lower olefins.

Carbon chain growth mechanism of higher alcohols synthesis from syngas on CoCu(100): A combined DFT and kMC study

Higher alcohols synthesis (HAS) directly from syngas, as environment-friendly liquid fuel or fuel additives, is regarded as a “green” route. It is essential for catalyst design to study reaction mechanism of HAS. To obtain carbon chain growth mechanism, the reaction network of HAS from syngas on CoCu(100) surface was systematically studied by density functional theory (DFT) calculation and kinetic Monte Carlo (kMC) simulation. Firstly, the energetics of the key elementary reactions of HAS on CoCu(100) were obtained by DFT calculation. And then the reaction processes of carbon chain growth were simulated with kMC method. Finally, the reaction mechanism of carbon chain growth on CoCu(100) was analyzed. The results show that CH is not easy to generate and easy to transform into CH2 on CoCu(100). CH2 and CH3 are easy to form and they are the key intermediates to achieve carbon chain growth. The addition of Co can promote the CO bond of CH2O scission to form CH2, inhibit the formation of CH3O, change the reaction pathway on CoCu(100), and thus improve the selectivity of C2+ alcohols.

Phase transformation assisted twinning in a face-centered-cubic FeCrNiCoAl0.36 high entropy alloy

The FeNiCoCr-based high entropy alloys (HEAs) exhibit excellent mechanical properties, such as twin-induced plasticity (TWIP) and phase transformation plasticity (TRIP) that can reach a remarkable combination of strength and ductility. In the present work, the face-centered-cubic (FCC) single-crystal FeNiCoCrAl0.36 HEAs were studied, using the density functional theory (DFT) combined with the phonon calculation to estimate the stacking fault energies, temperature-dependent phase stabilities of different structures. And the kinetic Monte Carlo (kMC) was used to predict the substructures evolution based on the transition state energies obtained from DFT calculations. We proposed two different formation paths of nano-twins in this Al-composited HEA and found that short-range hexagonal-close-packed (HCP)-stacking could occur in this HEA. The DFT calculations suggest that this HEA has negative stacking fault energy (SFE), HCP formation energy, and twin-formation energy at 0 K. Phonon calculations indicate that at the finite temperature, the competing FCC/HCP phase stability and propensity for twinning make it possible to form HCP-like twin boundaries. The kMC simulations suggest that under deformation, TWINs could form within the HCP laths which differs from the study of others. With the great agreement of results from kMC simulations and experiments, this twin-hcp laminated substructure formation path offers a new concept of designing TWIP HEAs containing tunable twin structures with HCP and TWIN lamellae structures, which could result in better mechanical properties of HEAs.