Search Results (13)

Atomic layer deposition (ALD) has become a versatile technique for atomic-level material synthesis and surface modification, with broad relevance in applications such as energy storage devices, integrated circuits, and membranes. The deposition is governed by a multitude of factors spanning multiple length/time scales, ranging from macroscopic transport phenomena to atomistic surface reactions. Owing to the intricate process coupling across these scales, a comprehensive evaluation of atomistic behavior under flow conditions remains highly challenging. To address this issue, a multiscale integrated simulation framework is developed in which precursor transport within high-aspect-ratio nanochannels is explicitly simulated using a finite volume method (FVM) and subsequently coupled with kinetic Monte Carlo (kMC) simulations to evaluate film growth characteristics. Based on the FVM results, the spatiotemporal distribution of precursor partial pressures along nanochannels is extracted and employed as input for a lattice-based kMC model, enabling atomistic resolution of the ALD process over the full deposition cycle. By coupling these two scales, key deposition metrics including step coverage and its temporal evolution under varying partial pressure conditions are quantitatively obtained, together with atomic-level smoothness and vacancy ratio. The kMC simulations further enable detailed visualization of film growth behavior on substrates under different precursor partial pressure environments. The results indicate that, within the microstructure, even when the overall precursor exposure is comparable, films formed near the entrance exhibit superior surface structure, as evidenced by higher atomic-level smoothness and reduced vacancy ratio, compared to those at the bottom. This study provides an engineering-feasible framework for evaluating atomistic film properties in ALD processes by consistently integrating fluid transport and surface reaction kinetics. Full article

We investigate the critical properties of kinetic continuous opinion dynamics using deep learning techniques. The system consists of N continuous spin variables in the interval $[-1,1]$. Dense neural networks are trained on spin configuration data generated via kinetic Monte Carlo simulations, accurately identifying the critical point on both square and triangular lattices. Classical unsupervised learning with principal component analysis reproduces the magnetization and allows estimation of critical exponents. Additionally, variational autoencoders are implemented to study the phase transition through the loss function, which behaves as an order parameter. A correlation function between real and reconstructed data is defined and found to be universal at the critical point. Full article

In the present work, we address the theory of the lattice-gas model to the study of intercalation materials by using a novel kinetic Monte Carlo (kMC) algorithm for the simulation of an electrochemical method of everyday use in R&D laboratories: constant-current chrono-potentiometric measurements. The main aim of the present approach is to show how to use these atomistic simulations to study intercalation materials used as electrodes in alkali-ion batteries under galvanostatic conditions. The framework can be applied to related areas. To accomplish this, we explain the electrochemical background, linking the continuum scale with the microscopic events of discrete simulations. A comprehensive theoretical approach developed in a previous work is used as a reference for this aim. The galvanostatic kMC algorithm proposed is explained in detail and is subject to validation tests. The present work may serve as a basis for future implementations of kMC under galvanostatic conditions to study phenomena beyond the applicability of simulations on the continuum scale. Full article

In the present paper, we discuss the thermodynamic and dynamic phase transition properties of the kinetic Blume–Capel model with spin-1, defined on non-regular lattices, namely decorated simple cubic, decorated triangular, and decorated square (Lieb) lattice geometries. Benefiting from the recent results obtained for the thermodynamic phase transitions of the aforementioned lattice topologies [Azhari, M. and Yu, U., J. Stat. Mech. (2022) 033204], we explore the variation of the dynamic order parameter, dynamic scaling variance, and dynamic magnetic susceptibility as functions of the amplitude, bias, and period of the oscillating field sequence. According to the simulations, a second-order dynamic phase transition takes place at a critical field period for the systems with zero bias. A particular emphasis has also been devoted to metamagnetic anomalies emerging in the dynamic paramagnetic phase. In this regard, the generic two-peak symmetric behavior of the dynamic response functions has been found in the slow critical dynamics (i.e. dynamic paramagnetic) regime. Our results yield that the characteristics of the dynamic phase transitions observed in the kinetic Ising model on regular lattices can be extended to such non-regular lattices with a larger spin value. Full article

Silicon, as the basic material of biochips and electronic devices, is often exposed to irradiation environments, and its radiation resistance has attracted much attention in recent decades. We calculated collision cascade in a silicon-based device under energetic Ar ions irradiation by using Monte Carlo and molecular dynamics simulations. The difference in vacancy probability density under different energetic incident ion irradiation is caused by the penetrating power and the straggling power of incident ions. The kinetic energy of an incident ion determines the size of local collision cascade density; a high energy incident ion can induce greater local collision cascade density. The efficiency of transferring energy from incident ions to target electrons at the silicon surface is more than in silicon, and the recoil atoms dissipate most of their energy at the lattice sites where they are stopping. These results provide more insight into the radiation resistance of silicon-based devices. Full article

A three-dimensional kinetic Monte Carlo methodology is developed to study the strained epitaxial growth of wurtzite GaN/AlN quantum dots. It describes the kinetics of effective GaN adatoms on an hexagonal lattice. The elastic strain energy is evaluated by a purposely devised procedure: first, we take advantage of the fact that the deformation in a lattice-mismatched heterostructure is equivalent to that obtained by assuming that one of the regions of the system is subjected to a properly chosen uniform stress (Eshelby inclusion concept), and then the strain is obtained by applying the Green’s function method. The standard Monte Carlo method has been modified to implement a multiscale algorithm that allows the isolated adatoms to perform long diffusion jumps. With these state-of-the art modifications, it is possible to perform efficiently simulations over large areas and long elapsed times. We have taylored the model to the conditions of molecular beam epitaxy under N-rich conditions. The corresponding simulations reproduce the different stages of the Stranski–Krastanov transition, showing quantitative agreement with the experimental findings concerning the critical deposition, and island size and density. The influence of growth parameters, such as the relative fluxes of Ga and N and the substrate temperature, is also studied and found to be consistent with the experimental observations. In addition, the growth of stacked layers of quantum dots is also simulated and the conditions for their vertical alignment and homogenization are illustrated. In summary, the developed methodology allows one to reproduce the main features of the self-organized quantum dot growth and to understand the microscopic mechanisms at play. Full article

A generic model was developed for studies of the polymerization process of regular branched macromolecules. Monte Carlo simulations were performed employing the Dynamic Lattice Liquid algorithm to study this process. A core-first methodology was used in a living polymerization of stars with up to 32 arms, and dendrimers consisted of 4-functional segments. The kinetics of the synthesis process for stars with different numbers of branches and dendrimers was compared. The size and structure of star-branched polymers and dendrimers during the synthesis were studied. The influence of the functionality of well-defined cores on the structure and on the dispersity of the system was also examined. The differences in the kinetics in the formation of both architectures, as well as changes to their structures, were described and discussed. Full article

Barite (Ba[SO₄]) is one of the promising candidates for sequestration of radioactive waste. Barite can incorporate radium (Ra) and form ideal solid solutions, i.e., (Ba,Ra)[SO₄]. Together with isostructural celestite (Sr[SO₄]), ternary solid solutions, (Ba,Sr,Ra)[SO₄], may exist in natural conditions. Our fundamental understanding of the dissolution kinetics of isostructural sulfates is critically important for a better risk assessment of nuclear waste repositories utilizing this mineral for sequestration. So far, the barite-water interface has been studied with experimental methods and atomistic computer simulations. The direct connection between the molecular scale details of the interface structure and experimental observations at the microscopic scale is not yet well understood. Here, we began to investigate this connection by using a kinetic Monte Carlo approach to simulate the barite dissolution process. We constructed a microkinetic model for the dissolution process and identified the reactive sites. Identification of these sites is important for an improved understanding of the dissolution, adsorption, and crystal growth mechanisms at the barite–water interface. We parameterized the molecular detachment rates by using the experimentally observed etch pit morphologies and atomic step velocities. Our parameterization attempts demonstrated that local lattice coordination is not sufficient to differentiate between the kinetically important sites and estimate their detachment rates. We suggest that the water structure and dynamics at identified sites should substantially influence the detachment rates. However, it will require more work to improve the parameterization of the model by means of Molecular Dynamics and ab initio calculations. Full article

Defects, e.g., Vacancies (Vs) and Defect-impurity centers, e.g., Nitrogen-Vacancy complexes (NVs), in group IV materials (diamond, SiC, graphene) are unique systems for Quantum Technologies (QT). The control of their positioning is a key issue for any realistic QT application and their tailored inclusion during controlled crystal-growth processes could overcome the limitations of other incorporation methods (e.g., ion implantation causing strong lattice damage). To date, the atomistic evolution regarding the growth of group IV crystals is barely known and this missing knowledge often results in a lack of process control in terms of mesoscopic crystal quality, mainly concerning the eventual generation of local or extended defects and their space distribution. We have developed Kinetic Monte Carlo models to study the growth kinetics of materials characterized by sp ${}^3$ bonding symmetries with an atomic-level accuracy. The models can be also coupled to the continuum simulation of the gas-phase status generated in the equipment to estimate the deposition rate and reproduce a variety of growth techniques (e.g., Chemical and Physical Vapour deposition, sublimation, etc.). Evolution is characterized by nucleation and growth of ideal or defective structures and their balance depends critically on process-related parameters. Quantitative predictions of the process evolution can be obtained and readily compared with the structural characterization of the processed samples. In particular, we can describe the surface state of the crystal and the defect generation/evolution (for both point and extended defects, e.g., stacking faults) as a function of the initial substrate conditions and the process parameters (e.g., temperature, pressure, gas flow). Full article

Kinetic Monte Carlo (kMC) methods have been used extensively for the study of crystal dissolution kinetics and surface reactivity. A current restriction of kMC simulation calculations is their limitation in spatial system size. Here, we explore a new and very fast method for the calculation of the reaction kinetics of a dissolving crystal, capable of being used for much larger systems. This method includes a geometrical approach, the Voronoi distance map, to generate the surface morphology, including etch pit evolution, and calculation of reaction rate maps and rate spectra in an efficient way, at a calculation time that was about 1/180 of the time required for a kMC simulation of the same system size at one million removed atoms. We calculate Voronoi distance maps that are based on a distance metric corresponding to the crystal lattice, weighted additively in relation to stochastic etch pit depths. We also show how Voronoi distance maps can be effectively parameterized by kMC simulation results. The resulting temporal sequences of Voronoi maps provide kinetic information. By comparing temporal sequences of kMC simulation and Voronoi distance maps of identical etch pit distributions, we demonstrate the opportunity of making specific predictions about the dissolution reaction kinetics, based on rate maps and rate spectra. The dissolution of an initially flat Kossel crystal surface served as an example to show that a sequence of Voronoi calculations can predict dissolution kinetics based on the information about the distribution of screw defects. The results confirm that a geometrical relationship exists between the material flux from the surface at a certain point and the distance (or, when considering anisotropy, a function of distance) to the nearest defect. In this study, for the sake of comparability, the calculations are made using input parameters directly derived from the kMC models operating at the atomic scale. We show that, using values of v(r_pit) and weighting factors obtained by kMC, the resulting surface morphologies and material flux are almost identical. This implies that discrete Voronoi calculations of starting and end points of the dissolution are sufficient to calculate material flux maps, without the time-consuming overhead of computing the interim reactions at the atomic-scale. This opens a promising new venue to efficiently upscale full-atomic kMC models to the continuum macroscopic level where reactive transport and Lattice Boltzmann calculations can be applied. Full article

Using quantum mechanical methods, in the framework of non-equilibrium Green’s function (NEGF) theory, we discuss the effects of the real space distribution of hydrogen adatoms on the electronic properties of graphene. Advanced methods for the stochastic process simulation at the atomic resolution are applied to generate system configurations in agreement with the experimental realization of these systems as a function of the process parameters (e.g., temperature and hydrogen flux). We show how these Monte Carlo (MC) methods can achieve accurate predictions of the functionalization kinetics in multiple time and length scales. The ingredients of the overall numerical methodology are highlighted: the ab initio study of the stability of key configurations, on lattice matching of the energetic configuration relation, accelerated algorithms, sequential coupling with the NEGF based on calibrated Hamiltonians and statistical analysis of the transport characteristics. We demonstrate the benefit to this coupled MC-NEGF method in the study of quantum effects in manipulated nanosystems. Full article

Through Monte Carlo simulations, we studied the critical properties of kinetic models of continuous opinion dynamics on ( $3,4,6,4$ ) and ( $3^4,6$ ) Archimedean lattices. We obtain $p_c$ and the critical exponents’ ratio from extensive Monte Carlo studies and finite size scaling. The calculated values of the critical points and Binder cumulant are $p_c=0.085\left(6\right)$ and $O_4^{*}=0.605\left(9\right)$ ; and $p_c=0.146\left(5\right)$ and $O_4^{*}=0.606\left(3\right)$ for ( $3,4,6,4$ ) and ( $3^4,6$ ) lattices, respectively, while the exponent ratios $\beta /\nu$ , $\gamma /\nu$ and $1/\nu$ are, respectively: $0.126\left(1\right)$ , $1.50\left(7\right)$ , and $0.90\left(5\right)$ for ( $3,4,6,4$ ); and $0.125\left(3\right)$ , $1.54\left(6\right)$ , and $0.99\left(3\right)$ for ( $3^4,6$ ) lattices. Our new results agree with majority-vote model on previously studied regular lattices and disagree with the Ising model on square-lattice. Full article