Random walk on distant mesh points Monte Carlo methods

A new technique for obtaining Monte Carlo algorithms based on the Markov chains with a finite number of states is suggested. Instead of the classical random walk on neighboring mesh points, a general way of constructing Monte Carlo algorithms that could be called random walk on distant mesh points is considered. It is applied to solve boundary value problems. The numerical examples indicate that the new methods are less laborious and therefore more efficient.In conclusion, we mention that all Monte Carlo algorithms are parallel and could be easily realized on parallel computers.     

Monte Carlo calculations of atoms and molecules

The variational and Green's function Monte Carlo (GFMC) methods can treat many interesting atomic and molecular problems. These methods can give chemical accuracy for up to 10 or so electrons. The various implementations of the GFMC method, including the domain Green's function method and the short-time approximation, are discussed. Results are presented for several representative atoms and molecules.     

高能伽玛相机探测量子效率的蒙特卡罗模拟

探测量子效率（DQE）可被用来精确描述图像噪声通过成像系统的传播特性。根据级联理论,简洁得出了零频噪声传播情况下成像系统的DQE 表达式。采用蒙特卡罗程序MCNP 计算了高能X射线的硬化能谱在六种常用闪烁晶体中的能量沉积分布以及高能伽玛相机的DQE,分析了闪烁晶体的材料和厚度对DQE的影响。模拟结果表明,高密度、高原子序数是影响DQE的主要因素,在同样厚度下,LuAP和LSO转换屏的探测量子效率最高。     

Kinetic Monte Carlo simulations of three-dimensional self-assembled quantum dot islands

By three-dimensional kinetic Monte Carlo simulations, the effects of the temperature, the flux rate, the total coverage and the interruption time on the distribution and the number of self-assembled InAs/GaAs(001) quantum dot(QD) islands are studied, which shows that a higher temperature, a lower flux rate and a longer growth time correspond to a better island distribution. The relations between the number of islands and the temperature and the flux rate are also successfully simulated. It is observed that for the total coverage lower than 0.5 ML, the number of islands decreases with the temperature increasing and other growth parameters fixed and the number of islands increases with the flux rate increasing when the deposition is lower than 0.6 ML and the other parameters are fixed.     

Monte Carlo Hamiltonian: Linear Potentials

We further study the validity of the Monte Carlo Hamiltonian method. The advantage of the method,in comparison with the standard Monte Carlo Lagrangian approach, is its capability to study the excited states. Weconsider two quantum mechanical models: a symmetric one V(x) = |x|/2; and an asymmetric one V(x) = ∞, forx ＜ 0 and V(x) = x, for x ≥ 0. The results for the spectrum, wave functions and thermodynamical observables are inagreement with the analytical or Runge-Kutta calculations.     

用量子蒙特卡罗方法研究二维超流-莫特绝缘体相变点附近的希格斯粒子

与伽利略不变性的超流体不同, 具有洛伦兹不变性的超流体中除了声子模之外, 还存在希格斯振幅模(Higgs amplitude mode). 在二维情况下, 由于存在十分剧烈的衰变成声子模的过程, 希格斯模是否仍然是一个能产生尖锐线性响应的激发子成为了一个问题. 近年来的进展最终对这一持续数十年的争论做出了肯定的回答, 证实了希格斯的可观测性. 在这里, 我们回顾一系列的数值方面的工作; 它们以二维超流体(superfluid)到莫特绝缘体(Mott insulator) 量子相变点(SF-MI QCP) 附近的具有洛伦兹不变性的超流体为对象, 成功探测到了希格斯模的线性响应信号. 特别是, 我们介绍了一种如何使用平衡态系统的蒙特卡罗算法计算强关联系统的延迟响应函数(retarded response function)的方法. 该方法主要包含两个核心步骤: 即通过路径积分表示下的蠕虫算法这一高效的蒙特卡罗算法计算平衡态系统的虚时间关联函数, 然后利用数值解析延拓方法从虚时间关联函数中获得实时间(实频率)的响应函数. 将该数值方法应用于二维SF-MI QCP附近的玻色-哈伯德模型(Bose-Hubbard Model), 结果表明尽管在超流相中, 希格斯模衰变过程非常剧烈, 但是在动能算符相对应的延迟响应函数的虚部中, 仍然可以观测到希格斯模所对应的尖锐的共振峰. 进一步的研究表明, 在莫特绝缘相, 甚至常流体相中, 也可能存在类似的共振峰信号. 由于可以在光晶格中超冷原子系统等凝聚态中观测到SF-MI QCP, 因此希格斯共振峰有望通过实验进行直接探测. 此外我们指出, 同样的希格斯共振峰还存在于所有和SF-MI QCP具有相同普适类((2+1)维相对论性U(1)临界性)的量子临界系统中.     

Monte Carlo Hamiltonian:Inverse Potential

The Monte Carlo Hamiltonian method developed recentlyallows to investigate the groundstate and low-lying excited states of a quantum system, using MonteCarlo (MC) algorithm with importance sampling. However, conventional MC algorithmhas some difficulties when applied to inverse potentials. We propose to use effective potential andextrapolation method to solve the problem. We present examples from the hydrogensystem.     

High-precision Monte Carlo test of the conformai-invariance predictions for two-dimensional mutually avoiding walks

Let _l be the critical exponent associated with the probability thatl independentN-step ordinary random walks, starting at nearby points, are mutually avoiding. Using Monte Carlo methods combined with a maximum-likelihood data analysis, we find that in two dimensions ₂=0.6240±0.0005±0.0011 and ₃=1.4575±0.0030±0.0052, where the first error bar represents systematic error due to corrections to scaling (subjective 95% confidence limits) and the second error bar represents statistical error (classical 95% confidence limits). These results are in good agreement with the conformal-invariance predictions ₂=5/8 and ₃=35/24.     

Solving quantum rotor model with different Monte Carlo techniques

We systematically test the performance of several Monte Carlo update schemes for the (2+1)d XY phase transition of quantum rotor model. By comparing the local Metropolis (LM), LM plus over-relaxation (OR), Wolff-cluster (WC), hybrid Monte Carlo (HM), hybrid Monte Carlo with Fourier acceleration (FA) schemes, it is clear that among the five different update schemes, at the quantum critical point, the WC and FA schemes acquire the smallest autocorrelation time and cost the least amount of CPU hours in achieving the same level of relative error, and FA enjoys a further advantage of easily implementable for more complicated interactions such as the long-range ones. These results bestow one with the necessary knowledge of extending the quantum rotor model, which plays the role of ferromagnetic/antiferromagnetic critical bosons or Z₂ topological order, to more realistic and yet challenging models such as Fermi surface Yukawa-coupled to quantum rotor models.     

Auxiliary-field Monte Carlo methods

I discuss Monte Carlo algorithms for quantum many-body systems that employ an auxiliary field to linearize a two-body interaction. These reduce the evaluation of the partition function to sampling many one-body evolutions in a fluctuating field. Fermions and bosons are treated on an equal footing. Applications to potential models and to quantum spin systems are discussed. This work was supported in part by the National Science Foundation, grants PHY82-07332 and PHY85-05682. The potential-model studies were done in collaboration with G. Sugiyama, while A. Khan and T. Troudet were responsible for the work on the quantum spin systems.     

Direct and reverse energy transport in systems of monomers and imperfect traps: Monte Carlo simulations

A Monte Carlo simulation of the concentration dependence of the fluorescence quantum yield _M and emission anisotropyr _M of a system containing dye molecules in the form of monomers M and clusters T (statistical pairs and trimers) playing the role of the imperfect traps for nonradiative excitation energy transfer (NET) has been carried out. The simulation has been made for determined values of Förster critical distancesR ₀ ^MM andR ₀ ^MT and for several values ofR ₀ ^TM andR ₀ ^TT , assuming that the energy may be transferred from M^* to T as well as from T^* to M (reverse nonradiative energy transfer, RNET). It was shown that the RNET process in the range of high concentrations may strongly change the values ofr _M as well as those of _M. For emission anisotropyr _M an effect of repolarization was observed which decreases rapidly with increasingR ₀ ^TM andR ₀ ^TT . A very good agreement between the simulation results of _M and the theoretical model with no adjustable parameters was found. In the model, the RNET process and influence of correlation between active molecules on energy migration among monomers were taken into account.     

The Convergence of Markov Chain Monte Carlo Methods: From the Metropolis Method to Hamiltonian Monte Carlo

From its inception in the 1950s to the modern frontiers of applied statistics, Markov chain Monte Carlo has been one of the most ubiquitous and successful methods in statistical computing. The development of the method in that time has been fueled by not only increasingly difficult problems but also novel techniques adopted from physics. Here, the history of Markov chain Monte Carlo is reviewed from its inception with the Metropolis method to the contemporary state‐of‐the‐art in Hamiltonian Monte Carlo, focusing on the evolving interplay between the statistical and physical perspectives of the method.     

Hierarchical Monte Carlo methods for fractal random fields

Two hierarchical Monte Carlo methods for the generation of self-similar fractal random fields are compared and contrasted. The first technique, successive random addition (SRA), is currently popular in the physics community. Despite the intuitive appeal of SRA, rigorous mathematical reasoning reveals that SRA cannot be consistent with any stationary power-law Gaussian random field for any Hurst exponent; furthermore, there is an inherent ratio of largest to smallest putative scaling constant necessarily exceeding a factor of 2 for a wide range of Hurst exponentsH, with 0.30<H<0.85. Thus, SRA is inconsistent with a stationary power-law fractal random field and would not be useful for problems that do not utilize additional spatial averaging of the velocity field. The second hierarchical method for fractal random fields has recently been introduced by two of the authors and relies on a suitable explicit multiwavelet expansion (MWE) with high-moment cancellation. This method is described briefly, including a demonstration that, unlike SRA, MWE is consistent with a stationary power-law random field over many decades of scaling and has low variance.     

A Theory on Flat Histogram Monte Carlo Algorithms

The flat histogram Monte Carlo algorithms have been successfully used in many problems in scientific computing.However, there is no a rigorous theory for the convergence of the algorithms. In this paper, a modified flat histogram algorithm is presented and its convergence is studied. The convergence of the multicanonical algorithm and the Wang-Landau algorithm is argued based on their relations to the modified algorithm. The numerical results show the superiority of the modified algorithm to the multicanonical and Wang-Landau algorithms. PACS number: 02.70.Tt, 02.50.Ng     

大粒子数二维硬核玻色子系统的量子蒙特卡罗模拟

采用随机级数展开的量子蒙特卡罗方法研究二维硬核的玻色-赫伯德模型的热力学性质. 首先通过算符变换将模型映射成为二维反铁磁准海森伯模型. 变换后的模型比通常的海森伯模型多一项, 该项正比于系统的格点总数 N, 对于大粒子数的系统, 该项使模拟耗时指数增加, 所以难以计算大粒子数系统.采用非局域操作循环更新后, 这个困难可以得到很好的解决, 可使粒子数总数增大到几千个.研究结果表明, 粒子数密度在0-0.5范围内增大时, 能量呈递减趋势, 并趋于某一定值, 随着正方晶格系统尺度增大, 能量也随之增大;正方晶格系统尺度一定时, 能量和磁化强度随着温度的升高而增大, 化学势的变化对能量和磁化强度没有影响, 能量随着正方晶格系统尺度增大而增大, 磁化强度却随之减小;正方晶格系统尺度一定时, 化学势的增大对比热没有影响, 随着温度的升高比热出现先增大后减小的趋势, 最后趋于某个值, 达到平衡, 而正方晶格系统尺度越大, 比热曲线增大部分的趋势越大, 减小部分的趋势也更明显, 参照朗道超流理论, 本文模拟的能量和比热曲线趋势与朗道二流体模型下He II的理论研究一致; 不同正方晶格系统尺度的影响不大, 均匀磁化率倒数在0-0.5(J/k_B)的低温范围内有很小的波动, J为耦合能, k_B为玻尔兹曼常数, 温度在0.5-2 (J/k_B)的范围内, 均匀磁化率的倒数随着温度的升高而增大, 且曲线的趋势显示了一种类似近藤行为.     

Kinetic Monte Carlo simulations of optimization of self-assembly quantum rings growth strategy based on substrate engineering

In this paper, the kinetic Monte Carlo simulations of the self-assembly quantum rings (QRs) based on the substrate engineering, which is related to the eventual shape of the formed quantum ring, are implemented. According to the simulation results, the availability of the QR with tunable size and the formation of smooth shape on the ideal flat substrate are checked. Through designing the substrate engineering, i.e., changing the depth, the separation and the ratio between the radius and the height of the embedded inclusions, the position and size of QR can be controlled and eventually the growth strategy of optimizing the self-assembly QRs is accomplished.     

Two-sided bounds on the free energy from local states in Monte Carlo simulations

We show that a precise assessment of free energy estimates in Monte Carlo simulations of lattice models is possible by using cluster variation approximations in conjunction with the local states approximations proposed by Meirovitch. The local states method (LSM) utilizes entropy expressions which recently have been shown to correspond to a converging sequence of upper bounds on the thermodynamic limit entropy density (i.e., entropy per lattice site), whereas the cluster variation method (CVM) supplies formulas that in some cases have been proven to be, and in other cases are believed to be, lower bounds. We have investigated CVM-LSM combinations numerically in Monte Carlo simulations of the two-dimensional Ising model and the two-dimensional five-states ferromagnetic Potts model. Even in the critical region the combination of upper and lower bounds enables an accurate and reliable estimation of the free energy from data of a single run. CVM entropy approximations are therefore useful in Monte Carlo simulation studies and in establishing the reliability of results from local states methods.     

Quantum dynamics at finite temperature: Time-dependent quantum Monte Carlo study

In this work we investigate the ground state and the dissipative quantum dynamics of interacting charged particles in an external potential at finite temperature. The recently devised time-dependent quantum Monte Carlo (TDQMC) method allows a self-consistent treatment of the system of particles together with bath oscillators first for imaginary-time propagation of Schrödinger type of equations where both the system and the bath converge to their finite temperature ground state, and next for real time calculation where the dissipative dynamics is demonstrated. In that context the application of TDQMC appears as promising alternative to the path-integral related techniques where the real time propagation can be a challenge.     

Monte Carlo simulation of magnetic nanostructured thin films

Using Monte Carlo simulation, we have compared the magnetic properties between nanostructured thin films and two-dimensional crystalline solids. The dependence of nanostructured properties on the interaction between particles that constitute the nanostructured thin films is also studied. The result shows that the parameters in the interaction potential have an important effect on the properties of nanostructured thin films at the transition temperatures.     

非均一相互作用能对超薄膜生长影响的Monte Carlo模拟研究

利用Monte Carlo (MC)模拟技术研究了非均一的吸附原子与基底相互作用能在一定的生长条件下对超薄膜生长过程的影响.非均一相互作用能是由基底表面原子在垂直和水平方向上实际位置与理想晶格原子位置的偏差所造成.本文用高斯分布来表示这种非均一相互作用能.模拟结果表明:非均一相互作用能对超薄膜的生长过程及薄膜的形貌有显著的影响.这种影响同时受到生长条件的限制,在中等温度时相互作用能的非均一性对岛的个数、平均大小的影响最显著;温度的增加在一定程度上可抵御相互作用能的非均一性对薄膜生长的影响. 关键词： 薄膜生长 Monte Carlo 模拟 相互作用能