Monte Carlo Study Research Articles

Insight into electronic and magnetic properties of Ba2NiOsO6 double perovskite: A Monte Carlo study and a comparative investigation between TB-mBJ and GGA+U

The structural, electronic, and magnetic properties of the Ba2NiOsO6 double perovskite were comprehensively studied using density functional theory (DFT) and Monte Carlo simulations. Based on DFT calculations, we found that the double perovskite is stable in the ferromagnetic phase. The compound exhibited thermodynamic stability, confirmed by its negative formation energy and phonon spectrum. The electronic and magnetic properties were analyzed using various approximations, including the Generalized Gradient Approximation (GGA), modified Becke–Johnson approximation (mBJ), and Hubbard correction (GGA+U). The electronic profile indicated a metallic nature with GGA and a half-metallic nature with GGA+U and mBJ. The total magnetic moment was approximately 4 μB across all approximations. The Monte Carlo simulations revealed intriguing phenomena, such as a tri-critical point, second-order and first-order phase transitions, a reentrant phenomenon, and both simple and double hysteresis loops.

The impact of mechanical strain on magnetic and structural properties of 2D materials: A Monte Carlo study.

The inherent flexibility of two-dimensional (2D) materials allows for efficient manipulation of their physical properties through strain application, which is essential for the development of advanced nanoscale devices. This study aimed to understand the impact of mechanical strain on the magnetic properties of two-dimensional (2D) materials using Monte Carlo simulations. The effects of several strain states on the magnetic properties were investigated using the Lennard-Jones potential and bond length-dependent exchange interactions. The key parameters analyzed include the Lindemann coefficient, radial distribution function, and magnetization in relation to temperature and magnetic field. The results indicate that applying biaxial tensile strain generally reduces the critical temperature (Tc). In contrast, the biaxial compressive strain increased Tc within the elastic range, but decreased at higher strain levels. Both compressive and tensile strains significantly influence the ferromagnetic properties and structural ordering, as evidenced by magnetization hysteresis. Notably, pure shear strain did not induce disorder, leaving the magnetization unaffected. In addition, our findings suggest the potential of domain-formation mechanisms. This study provides comprehensive insights into the influence of mechanical strain on the magnetic behavior and structural integrity of 2D materials, offering valuable guidance for future research and advanced material design applications.

Estimation of Dose Perturbation Due to the Presence of Metal Hip Prostheses in Radiotherapy with Electron and Photon Beams: A Monte Carlo Study

Purpose: The impact of various hip prosthesis materials on the amount of dose perturbation generated by 9 MeV electron and 18 MV photon beams during pelvic radiation therapy is analyzed in this research. Materials and Methods: The Varian 2100 C/D LINAC head for the electron (9 MeV) and photon (18 MV) modes and a water phantom with a realistic hip prosthesis were modeled using the Monte Carlo (MC) code; MCNPX (Ver. 2.6.0) and were benchmarked for measurement. Four different materials, including Cobalt–Chromium–Molybdenum-alloy (CCM), Stainless Steel (SS), Titanium, and Titanium-alloy (Ti-alloy) were evaluated. The changes in electron and photon fluences and dose perturbations due to the presence of the hip prostheses were investigated. Results: An increased dose of 13.29%, 13.77%, 6.16%, and 5.93% for 9 MeV and 30.43%, 33.05%, 10.89%, and 11.27% for 18 MV was calculated for Ti-alloy, Ti, CCM, and SS prosthesis, respectively. At 0.5 mm distance from the prosthesis, the Electron Backscatter Factor (EBF) of 1.31, 1.33, 1.15, and 1.14 for 9 MeV and 1.32, 1.34, 1.11, and 1.12 for 18 MV was calculated for Ti-alloy, Ti, CCM, and SS prosthesis, respectively. Dose perturbation is higher at a near distance from the prosthesis; by reducing the distance from the Ti-alloy prosthesis (1.5 to 0.5 mm), an increased dose of 7.70% and 5.54% resulted in 18 MV and 9 MeV, respectively. The dose decreases of up to 21% behind the hip prosthesis were calculated for 18 MV. Conclusion: It is essential to consider the dose perturbation due to the presence of a hip prosthesis to achieve the optimal treatment planning in radiotherapy.

Calculation of Dose Perturbation in Radiotherapy of Head and Neck Tumors Due to the Presence of Dental Implants: A Monte Carlo Study

Purpose: The presence of a dental implant across the irradiation beam has the potential to perturb the dose distribution. In this study, the effect of different commercial dental implants on dose distribution was investigated in electron beam therapy. Materials and Methods: The Varian 2100 C/D linear accelerator (Linac) head was modeled precisely with proper components for electron mode (6 and 9 MeV) by MCNPX 2.6.1 and was benchmarked according to the International Atomic Energy Agency (IAEA) protocol, TRS -398. Dose distribution was calculated for Six different implant materials, including Titanium, Titanium alloy, Zirconia (Y-TZP), Zirconium oxide, Alumina, and PolyetherEtherKetone (PEEK), and for Four different scenarios. Results: The highest and lowest increasing doses occurred for Y-TZP (114.44% and 108.69% for 6 and 9 MeV, respectively) and PEEK (104.85% and 98.84% for 6 and 9 MeV, respectively) directly in front of the implant, respectively. By removing an implant from the jaw, an increasing dose was not seen, but an increasing dose occurred behind its depths in the bone region (31.81 %). Conclusion: The amount of dose perturbation due to the dental implant's presence depends on the beam energy, mass density, and atomic numbers of implants. Maximum and minimum increased doses were estimated for Y-TZP and PEEK implants, respectively. Considering the correction factors due to the presence of high density and atomic number dental implants are essential to estimate the accurate dose delivery in radiotherapy with electron beams.

Nematic Superconductivity and Its Critical Vestigial Phases in the Quasicrystal.

We propose a general mechanism to realize nematic superconductivity (SC) and reveal its exotic vestigial phases in the quasicrystal (QC). Starting from a Penrose-Hubbard model, our microscopic studies suggest that the Kohn-Luttinger mechanism driven SC in the QC is usually gapless due to violation of Anderson's theorem, rendering that both chiral and nematic SCs are common. The nematic SC in the QC can support novel vestigial phases driven by pairing phase fluctuations above its T_{c}. Our combined renormalization group and MonteCarlo studies provide a phase diagram in which, besides the conventional charge-4e SC, two critical vestigial phases emerge, i.e., the quasinematic (QN) SC and QN metal. In the two QN phases, discrete lattice rotation symmetry is counterintuitively "quasibroken" with power-law decaying orientation correlation. They separate the phase diagram into various phases connected via Berezinskii-Kosterlitz-Thouless (BKT) transitions. These remarkable critical vestigial phases, which resemble the intermediate BKT phase in the q state (q≥5) clock model, are a consequence of the fivefold (or higher) anisotropy field brought about by the unique QC symmetry, which are absent in conventional crystalline materials.

Statistical Identification of Independent Shocks with Kernel-based Maximum Likelihood Estimation and an Application to the Global Crude Oil Market

Independent component analysis has emerged as a promising approach for revealing structural relationships in multivariate dynamic systems, particularly in scenarios with limited knowledge of causal patterns. This article introduces a robust kernel-based maximum likelihood (KML) estimation method that accommodates the distributional characteristics of the structural sources of data variation. Our Monte Carlo study demonstrates the superior performance of the KML estimator compared to existing approaches for independence-based identification. Moreover, the proposed method enables partial identification and dimension reduction even in the presence of dependent shocks. We illustrate the benefits of our approach by applying it to the global oil market model of Kilian, highlighting its ability to capture unmodeled higher-order dependence between oil supply and speculative oil demand shocks.

Non‐causal and non‐invertible ARMA models: Identification, estimation and application in equity portfolios

The mixed causal‐non‐causal invertible‐non‐invertible autoregressive moving‐average (MARMA) models have the advantage of incorporating roots inside the unit circle, thus adjusting the dynamics of financial returns that depend on future expectations. This article introduces new techniques for estimating, identifying and simulating MARMA models. Although the estimation of the parameters is done using second‐order moments, the identification relies on the existence of high‐order dynamics, captured in the high‐order spectral densities and the correlation of the squared residuals. A comprehensive Monte Carlo study demonstrated the robust performance of our estimation and identification methods. We propose an empirical application to 24 portfolios from emerging markets based on the factors: size, book‐to‐market, profitability, investment and momentum. All portfolios exhibited forward‐looking behavior, showing significant non‐causal and non‐invertible dynamics. Moreover, we found the residuals to be uncorrelated and independent, with no trace of conditional volatility.

Monte Carlo simulations of glass-forming liquids beyond Metropolis.

Monte Carlo simulations are widely employed to measure the physical properties of glass-forming liquids in thermal equilibrium. Combined with local Monte Carlo moves, the Metropolis algorithm can also be used to simulate the relaxation dynamics, thus offering an efficient alternative to molecular dynamics. Monte Carlo simulations are, however, more versatile because carefully designed Monte Carlo algorithms can more efficiently sample the rugged free energy landscape characteristic of glassy systems. After a brief overview of Monte Carlo studies of glass-formers, we define and implement a series of Monte Carlo algorithms in a three-dimensional model of polydisperse hard spheres. We show that the standard local Metropolis algorithm is the slowest and that implementing collective moves or breaking detailed balance enhances the efficiency of the Monte Carlo sampling. We use time correlation functions to provide a microscopic interpretation of these observations. Seventy years after its invention, the Monte Carlo method remains the most efficient and versatile tool to compute low-temperature properties in supercooled liquids.

First-principles quantum Monte Carlo study of charge-carrier mobility in organic molecular semiconductors

We present a first-principles numerical study of charge transport in a realistic two-dimensional tight-binding model of organic molecular semiconductors. We use the hybrid Monte Carlo (HMC) algorithm to simulate the full quantum dynamics of phonons and either single or multiple charge carriers without any tunable parameters. We introduce a number of algorithmic improvements, including efficient Metropolis updates for phonon fields based on analytical insights, which lead to negligible autocorrelation times and allow sub-per-mille precisions to be reached at a low computational cost of O(1) CPU hours. Our simulations produce charge-mobility estimates that are in good agreement with experiments and that also justify the phenomenological transient localization approach. Published by the American Physical Society 2024

Quantum Monte Carlo study of quantized muon in molecular systems

Quantum Monte Carlo study of quantized muon in molecular systems

Multivariate normality tests with two-step monotone missing data: a critical review with emphasis on the different methods of handling missing values

Two-step monotone missing data is a special case of missing data that attracted the attention of researchers since it not only appears frequently in applications but also since under the hypothesis of multivariate normality a unique and explicit solution to the likelihood equations exists. This importance prompted Yamada et al. [Kurtosis tests for multivariate normality with monotone incomplete data. Test. 2015;24:532–557. doi: 10.1007/s11749-014-0423-1] and Kurita and Seo [Multivariate normality test based on kurtosis with two-step monotone missing data. J Multivar Anal. 2022;188:104–824. doi: 10.1016/j.jmva.2021.104824] to propose four procedures in total for testing multivariate normality with two-step monotone missing data. The aim of this paper is twofold. On the one, to compare, for the first time, the performance of the four aforementioned multivariate normality tests via a Monte Carlo study. The second aim of the paper is to compare the performance of the four existing testing procedures with the respective procedures derived as a combination of 12 popular methods for handling incomplete data and 6 multivariate normality tests for complete data. We show that in several cases the tests proposed by Yamada et al. [Kurtosis tests for multivariate normality with monotone incomplete data. Test. 2015;24:532–557. doi: 10.1007/s11749-014-0423-1] and Kurita and Seo [Multivariate normality test based on kurtosis with two-step monotone missing data. J Multivar Anal. 2022;188:104–824. doi: 10.1016/j.jmva.2021.104824] are unable to achieve the nominal significance level. At the same time, no single procedure was found to be the most powerful in all situations of multivariate alternatives considered. As a consequence, the use of specific combinations of methods of handling missing data and testing multivariate normality with complete data is recommended.

Financial Uncertainties in EPS and Share Prices: A Monte Carlo Study on U.S. Business Services MNCs

Financial Uncertainties in EPS and Share Prices: A Monte Carlo Study on U.S. Business Services MNCs

Magnetic and thermodynamic properties of mixed spin-3/2 and spin-3 Ising ferrimagnets on a 2D triangular lattice: Monte Carlo study

Monte Carlo methods in the presence and absence of an external magnetic field were used to investigate the thermodynamic and magnetic properties of the mixed spins (3/2, 3) Ising ferrimagnets in a 2D triangular lattice consisting of sublattices A, B, and C. Two types of mixing were considered: S→=(SA,SB,SC)=(3/2,3,3) (Model I) and S→=(SA,SB,SC)=(3/2,3,3/2) (Model II). In contrast to bipartite lattices, the antiferromagnetic coupling between spin-3/2 and spin-3 in the triangular lattice leads to geometric frustrations that impact magnetic properties. Determination of ground state phase diagrams, combined with verification of whether or not there was magnetic hysteresis when crossing each transition line, revealed first- and second-order phase transition lines, as well as multicritical points. The temperature investigation in the absence of an external magnetic field revealed rich magnetic properties such as 1st- and 2nd-order phase transitions, N- and L-type compensation points, tricritical points, and critical endpoints, as well as M-, P-, Q-, R- and S-type total magnetization behaviours. The effect of the crystal field on finite-temperature phase diagrams and compensation behaviour was also carried out. As a result, the critical temperatures of Model II become constant when the crystal field is sufficiently strong. In contrast, several critical values of the crystal field for which the critical temperature is zero were identified for Model I. In the presence of a magnetic field, hysteresis behaviour and associated magnetic properties such as coercivity and magnetic remanence were investigated. The effect of crystal field and temperature was explored. Hysteresis of one to four loops were found at low temperatures when the crystal field varied. Finally, as the temperature rises, the hysteresis loops' area decreases to zero at high temperatures.

Selection of Tuning Parameter and Comparison of Lasso and Adaptive Lasso on ZYZ Condition: A Monte Carlo Study

One of the fundamental objectives of statistics is to achieve accurate predictions. In high-dimensional settings (where the number of variables, p, exceeds the number of observations, n), the performance of ordinary least squares (OLS) is often suboptimal due to its high variance, which leads to lower prediction accuracy. Shrinking the variables is a promising approach, and methods such as ridge regression, elastic net, lasso, and adaptive lasso are well-known techniques for this purpose. While variable shrinkage introduces a small bias, it significantly reduces the variance compared to OLS. The effectiveness of shrinkage methods largely depends on the selection of the tuning parameter. Cross-validation and the Bayesian Information Criterion (BIC) are commonly used for this purpose, and an improved version of BIC has shown impressive results.???????? = (5.6,5.6,5.6,0), ???????? = (3,1.5,0,0,2,0,0,0), ???????? = (0.85,0.85,0.85,0) are the multiple regression models which are compared.

Using ligand regulation, metal replacement, and ligand doping strategies on Zr-FUM to improve methane separation from coalbed gas

Developing adsorbents suitable for industrial applications that can effectively enhance the separation of methane (CH4) from nitrogen (N2) in coalbed gas is crucial to improve energy recovery and mitigate greenhouse gas emissions. In this study, three modification strategies were implemented on Zr-FUM, including ligand regulation, metal replacement, and ligand doping, to synthesize Zr-FDCA, Al-FUM, and Zr-FUM-FA, with the aim of improving the performance of CH4/N2 separation under humid conditions. The results demonstrated that the promotion of robust orbital overlap and strengthened electrovalent bonding on adsorbents can selectively enhance CH4 adsorption. As a result, Zr-FUM-FA achieved a saturated CH4 adsorption capacity of 1.37 mmol/g, a CH4 working window of 307 s, and a CH4/N2 sorbent selection parameter (Ssp) of 47.31, exceeding the performance of most reported adsorbents. Analyses of the pore structure, surface morphology, and functional groups revealed that the presence of an ultramicropore proximity to CH4, reduced static resistance, and enhanced electrovalent bond were key factors for CH4 separation. Grand Canonical Monte Carlo and Density Functional Theory studies indicated that the introduction of -C-H- in FA played a crucial role in enhancing CH4 adsorption. Optimization of adsorption parameters using the Aspen adsorption package showed that in a dual-adsorbent bed system, the recovery and purity of CH4 in Zr-FUM-FA reach 99.5% and 97.3%, respectively, providing important theoretical support for the improvement of CH4 recovery in the pressure swing adsorption process from coalbed gas.

Weak identification in discrete choice models

We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that when the null hypothesis of weak identification is rejected, Wald-based inference can be carried out using standard formulas and critical values. A Monte Carlo study compares our proposed testing approach against commonly applied weak identification tests. The results simultaneously demonstrate the good performance of our approach and the fundamental failure of using conventional weak identification tests for linear models in the discrete choice model context. Lastly, we apply our approach in two empirical examples: married women labor force participation, and US food aid and civil conflicts.

A Comparing Robust Wilks’ statistics in Multivariate Multiple Linear Regression

In multivariate linear regression, the classical Wilks’ statistic is the most used method to test hypotheses, which is extremely responsive to the effect of outliers. Many authors have examined the non-robust test statistic established on normal theories for various cases. In this study, we constructed a robust version of Wilks’ statistic relying on a reweighed minimum covariance determinant estimator, that relies on the weight of the observations, with the weights being determined by using the Hampel weight function and Huber weight function. A comparison of the suggested statistics with the regular Wilks’ statistic has been discussed. Monte Carlo studies are used to evaluate how well test statistics work with different datasets. So, this study looked at how two different test statistics perform under a normal distribution. The first is the classical Wilks’ statistic, and the second is a proposed new one. For both of them, the rate of type I errors and the power of the tests were close to the expected significance levels. In situations where the distribution is contaminated, the proposed statistical method works best. If the data has been corrupted or affected somehow, this approach seems to perform the best out of the options

Melting and freezing of a skyrmion lattice

We report comprehensive Monte-Carlo studies of the melting of skyrmion lattices (SkL) in systems of small, medium, and large sizes with the number of skyrmions ranging from 103to over 105. Large systems exhibit hysteresis similar to that observed in real experiments on the melting of SkLs. For sufficiently small systems which achieve thermal equilibrium, a fully reversible sharp solid-liquid transition on temperature with no intermediate hexatic phase is observed. A similar behavior is found on changing the magnetic field that provides the control of pressure in the SkL. We find that on heating the melting transition occurs via a formation of grains with different orientations of hexagonal axes. On cooling, the fluctuating grains coalesce into larger clusters until a uniform orientation of hexagonal axes is slowly established. The observed scenario is caused by collective effects involving defects and is more complex than a simple picture of a transition driven by the unbinding and annihilation of dislocation and disclination pairs.

Robust estimate for count time series using GLARMA models: An application to environmental and epidemiological data

The Generalized Linear Autoregressive Moving Average (GLARMA) model has been used in epidemiological studies to evaluate the impact of air pollutants on health. Due to the nature of the data, a robust approach for the GLARMA model is proposed here based on the robustification of the quasi-likelihood function. Outlying observations are bounded separately by weight functions on covariates and the Huber loss function on the response variable. Some technical issues related to the robust approach are discussed and a Monte Carlo study revealed that the robust approach is more reliable than the classic one for contaminated data with additive outliers. The real data analysis investigates the impact of PM10 in the number of deaths by respiratory diseases in Vitória, Brazil.

Diffusion behaviors of binary mixtures of alkanes and aromatics through ZSM‐5 zeolite: A kinetic Monte Carlo study

AbstractDiffusion of hydrocarbon species in an MFI‐type zeolite was investigated using a coarse‐grained approach combined with Kinetic Monte Carlo (KMC) simulations. The model was employed to capture and isolate the essential characteristics of hydrocarbon diffusion such as molecular pushing, passing, and blocking. A modified Lennard‐Jones type forcefield was used to approximate interactions between molecules, and molecules with the oxygen in the zeolite lattice. The basis for the rate expressions is configurational diffusion theory, which has been adjusted to account for an accurate representation of the motions of hydrocarbon molecules trapped in the zeolite. Diffusion coefficients were estimated for low and high loading of single hydrocarbons as well as binary mixtures. In all cases studied, reasonable agreement was achieved with reported experimental data and molecular dynamics simulations. The model is conceptualized as an analytical tool that may be used to address key engineering topics such as applications of zeolites as size‐selective barriers.