Kinetic Monte Carlo simulations of heteroepitaxial growth with an atomistic model

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蒙特卡罗法

蒙特卡罗法

蒙特卡罗法简单介绍和案例蒙特卡罗法历史悠久。

1773年法国G.-L.L.von 布丰曾通过随机投针试验来确定圆周率π的近似值,这就是应用这个方法的最早例子。

蒙特卡罗是摩纳哥著名赌城,1945年 J.von 诺伊曼等人用它来命名此法,沿用至今。

数字计算机的发展为大规模的随机试验提供了有效工具,遂使蒙特卡罗法得到广泛应用。

在连续系统和离散事件系统的仿真中,通常构造一个和系统特性相近似的概率模型,并对它进行随机试验,因此蒙特卡罗法也是系统仿真方法之一。

对于蒙特卡罗技术应用于不可预见费的估算的研究,是对蒙特卡罗技术应用的拓展,能更好地了解尝试其在项目管理方面更多的应用,用其解决项目管理的问题。

用蒙特卡罗技术研究不可预见费,尝试用蒙特卡罗解决一般项目的不可预见费求取问题,避免不可预见费过高过低的问题。

蒙特卡洛方法的基本思想是:将符合一定概率分布的大量随机数作为参数带入数学模型,求出所关注变量的概率分布,从而了解不同参数对目标变量的综合影响以及目标变量最终结果的统计特性。

蒙特卡洛方法的基本原理简单描述如下:假定函数),...,,(21nx x x f y =,蒙特卡洛方法利用一个随机数发生器通过抽样取出每一组随机变量 (ni i i x x x ,...,,21),然后按),...,,(21n x x x f y =的关系式确定函数的值),...,,(21ni i i i x x x f y =。

反复独立抽样(模拟)多次(i=1,2,…),便可得到函数的一组抽样数据(n y y y ,...,,21),当模拟次数足够多时,便可给出与实际情况相近的函数y 的概率分布与其数字特征。

蒙特卡罗法(Monte Carlo Simulation )也称随机模拟,它主要依据概率分布对随机变量进行抽样,然后将样本带入数学模型进行计算得到应变量。

虽然蒙特卡罗模拟技术只给出的是统计估计而非精确的结果且应用其研究问题需要花费大量的计算时间,但它对问题的维数不敏感,对求解对象是线性问题与否也没有原则性要求,因此在复杂系统的不确定分析中,蒙特卡罗方法成为不可或缺的手段。

蒙特卡罗算法

蒙特卡罗算法

xi 1 6xi mod11, ui+1 xi 1 /11 (a 6, m 11 )
如果令 a 3, x0 1 ,得到序列: 1,3,9,5, 4,1,3,9........ 如果令 a 3, x0 2, 得到序列: 2, 6, 7,10,8, 2, 6.......
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3. 指数分布 指数分布随机变量X的概率密度为
e x , x 0 f ( x) 0, x 0
指数分布常用来描述寿命问题.
15
二.随机数的生成
1.蒙特卡罗模拟的关键是生成优良的随机数。 2.在计算机实现中,我们是通过确定性的算法生成 随机数,所以这样生成的序列在本质上不是随机 的,只是很好的模仿了随机数的性质(如可以通过 统计检验)。我们通常称之为伪随机数(pseudorandom numbers)。 3.在模拟中,我们需要产生各种概率分布的随机数, 而大多数概率分布的随机数产生均基于均匀分布 U(0,1)的随机数。
2
基本思想很早以前就被人们所发现和 利用。17世纪,人们就知道用事件发 生的“频率”来决定事件的“概率”。 19世纪人们用投针试验的方法来决定π。 高速计算机的出现,使得用数学方法 在计算机上大量模拟这样的试验成为 可能。
3
从Buffon(蒲丰)投针问题谈起
Buffon 投针问题:平面上画很多平行线,间距为 a.向此平面投掷长为 l ( l < a) 的 针,此针与任一平行线相交的概率 p。
Monte Carlo Simulation Methods (蒙特卡罗模拟方法)
主要内容: 一. M-C方法概述. 二. 随机数的生成. 三. 模拟训练. 四. 实验题目.
成信院数学与信息科学系 李胜坤

蒙特卡罗方法 (Monte Carlo simulation)教材

蒙特卡罗方法 (Monte Carlo simulation)教材

2019/4/15
Monte Carlo模拟
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2.Monte Carlo方法简史
Stanislaw Ulam (1909-1984)
S. Ulam is credited as the inventor of Monte Carlo method in 1940s, which solves mathematical problems using statistical sampling.
1
Monte Carlo模拟
第一章 引言 (Introduction)
1. 2. 3. 4. Monte Monte Monte Monte Carlo方法 Carlo方法简史 Carlo模拟的应用 Carlo算法的主要组成部分
2019/4/15
Monte Carlo模拟
2
1.Monte Carlo方法
2019/4/15
Monte Carlo模拟
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2.Monte Carlo方法简史 Buffon投针实验
1768年,法国数学家Comte de Buffon利用投针实验估 计的值
L
d
p
2019/4/15
d
2L
Monte Carlo模拟
6
2.Monte Carlo方法简史
Problem of Buffon’s needle: If a needle of length l is dropped at random on the middle of a horizontal surface ruled with parallel lines a distance d > l apart, what is the probability that the needle will cross one of the lines?

用Monte+Carlo法模拟纯铜的静态再结晶

用Monte+Carlo法模拟纯铜的静态再结晶

5期佟铭明等:用MonteCarlo法模拟纯铜的静志再结晶487l_5初始条件r在模拟中采用100×i00个正六边形单元组成的正方形阵列,不同的灰度代表不同的晶体取向.向系统中一次加入100个晶胚.温度为400℃,应变率为o.05S~,纯铜(99.995%)单位长度的晶界能(二维)【511≈63x10~J/m,初始状态晶粒大小约为10pro所有计算结果均为计算五次后数值的甲均值.2模拟结果与分析未变形时体系的原始组织是经过170MCS的正常品粒长大模拟后得到的(图la),图1中不同的衬度代表不同的晶粒取向(晶界上的锯齿是由于计算机屏幕的分辨率较低造成的,当分辨率足够高时晶界可以变得很光滑).囝1模拟体系的微观组织MicrostructureofthemodeledsystemwithdifferentMCSandstoredenergy(a)'OMCSFig.1(b)2MCS,H=l0x10—61.(c)2MCS,H=I95×10—61,(d)5MCS,H=20x1067,(e)5MCS,H=2.05×10~-y,(f)5MCS,H=30x10“?再结晶是一个存储能量的释放过程,所以在一定温度下静态再结晶行为完全由体系的初始变形状态(即初始存储能)决定为了研究初始存储能对静态冉结晶的影响,分别向每个单元中加入由塑性变形导致的初始存储能H。

为1.o×10一61,1.2×10—61.14×10一61,1.6×10一61,l8×lo一6’,1.9×10—6-y,1,95x106-y,2Oxl061,2.05×10—61,21X10—61,22x10—6~,24×10—61,2.6×10—61,2.8×10“1,30x10“1,计算“上15种条件下的静态再结晶过程.用Monte Carlo法模拟纯铜的静态再结晶作者:佟铭明, 莫春立, 李殿中, 李依依作者单位:中国科学院金属研究所刊名:材料研究学报英文刊名:CHINESE JOURNAL OF MATERIALS RESEARCH年,卷(期):2002,16(5)被引用次数:8次参考文献(8条)1.A Manonukul;F.P.E.Dunne"Initiation of Dynamic Recrystallization under Inhomogeneous Stress State in Pure Copper " 1999(47)2.B Radhakrishnan;G.B.Sarma;T.Zacharia"Modeling the Kinetics and Microstructural Evolution during static Recrystallization-Mongte Carlo Simulation of Recrystallization " 1998(12)3.G S Srest;D.J.Srolovitz;M.P.Anderson Computer Simulation of Grain Growth- IV. Anisotropic Grain Boundary Energies 1985(03)4.P Peczak;M.J.Luton A Monte Carlo Study of the Influence of Dynamic Recovery on Dynamic Recrystallization 1993(01)5.D J Srolovitz;M.P. Anderson;G.S.Grest Computer Simulation of Grain Growth- III. Influence of A Particle Dispersion 1984(09)6.D J Srolovitz;G.S.Grest;M.P. Anderson Computer Simulation of Grain Growth- V. Abnormal Grain Growth 1985(12)7.M P Anderson;D.J.Srolovitz;G.S.Grest;P.S.Sahni Computer Simulation of Grain Growth- I . Kinetics 1984(05)8.Yoshiyuki SAITO;Masato Enomoto Monte Carlo Simulation of Grain Growth 1992(03)引证文献(8条)1.王狂飞.莫亚林.夏立军.米国发.傅恒志15SiMn钢奥氏体等温相变的元胞自动机模拟[期刊论文]-特种铸造及有色合金 2009(7)2.王狂飞.张锦志.米国发.孙瑞霞晶粒生长的Monte Carlo法模拟研究进展[期刊论文]-热加工工艺 2009(10)3.王狂飞.莫亚林.夏立军.米国发.傅恒志25SiMn钢奥氏体等温相变过程的数值模拟[期刊论文]-铸造 2008(8)4.王狂飞.孙瑞霞.王继红碳含量对奥氏体等温相变影响的数值模拟[期刊论文]-热加工工艺 2008(16)5.方斌.黄传真.许崇海.刘增文.王景海材料微观组织演变的蒙特卡洛仿真原理及应用[期刊论文]-材料导报2007(2)6.郑成武.兰勇军.肖纳敏.李殿中.李依依热变形低碳钢中奥氏体静态再结晶介观尺度模拟[期刊论文]-金属学报2006(5)7.元胞自动机方法模拟再结晶过程的建模[期刊论文]-机械工程材料 2005(10)8.兰勇军低碳钢奥氏体—铁素体相变介观模拟计算[学位论文]博士 2005本文链接:/Periodical_clyjxb200205007.aspx。

蒙特卡洛模拟

蒙特卡洛模拟

蒙特卡洛模拟风险分析是我们制定的每个决策的一部分。

我们一直面对着不确定,不明确和变异。

甚至我们无法获得信息,我们不能准确的预测未来。

蒙特卡洛模拟( Monte Carlo simulation)让您看到了您决策的所有可能的输出,并评估风险,允许在不确定的情况下制定更好的决策。

什么是蒙特卡洛模拟( Monte Carlo simulation)蒙特卡洛模拟( Monte Carlo simulation)是一种计算机数学技术,允许人们在定量分析和决策制定过程中量化风险。

这项技术被专家们用于各种不同的领域,比如财经,项目管理,能源,生产,工程,研究和开发,保险,石油&天然气,物流和环境。

蒙特卡洛模拟( Monte Carlo simulation)提供给了决策制定者大范围的可能输出和任意行动选择将会发生的概率。

它显示了极端的可能性-最的输出,最保守的输出-以及对于中间路线决策的最可能的结果。

这项技术首先被从事原子弹工作的科学家使用;它被命名为蒙特卡洛,摩纳哥有名的娱乐旅游胜地。

它是在二战的时候被传入的,蒙特卡洛模拟( Monte Carlo simulation)现在已经被用于建模各种物理和概念系统。

蒙特卡洛模拟( Monte Carlo simulation)是如何工作的蒙特卡洛模拟( Monte Carlo simulation)通过构建可能结果的模型-通过替换任意存在固有不确定性的因子的一定范围的值(概率分布)-来执行风险分析。

它一次又一次的计算结果,每次使用一个从概率分布获得的不同随机数集。

根据不确定数和为他们制定的范围,蒙特卡洛模拟( Monte Carlo simulation)能够在它完成计算前调用成千上万次的重复计算。

蒙特卡洛模拟( Monte Carlo simulation)产生可能结果输出值的分布。

通过使用概率分布,变量能够拥有不同结果发生的不同概率。

概率分布是一种用来描述风险分析的变量中的不确定性的更加可行的方法。

MonteCarlo模拟教程

MonteCarlo模拟教程
蒙特卡罗方法的关键步骤在于随机数的产生, 计算机产生的随机数都不是真正的随机数(由算 法确定的缘故),如果伪随机数能够通过一系列 统计检验,我们也可以将其当作真正的随机数 使用。
rand('seed',0.1);
rand(1) %每次运ra行nd程('s序tat产e',s生um的(1值00*是clo相ck同)*r的and);
1901 3408
3.1415929
蒙特卡罗投点法是蒲丰投针实验的推广:
在一个边长为a的正方形内随机投点,
该点落在此正方形的内切圆中的概率 y
(a/2,a/2)
应为该内切圆与正方形的面积比值,
即 πa/22 : a2 π/4
n=10000; a=2; m=0; for i=1:n
ox
x=rand(1)*a; y=rand(1)*a;
举例
例1 在我方某前沿防守地域,敌人以一个炮排(含两门火炮) 为单位对我方进行干扰和破坏.为躲避我方打击,敌方对其阵地 进行了伪装并经常变换射击地点.
经过长期观察发现,我方指挥所对敌方目标的指示有50%是准 确的,而我方火力单位,在指示正确时,有1/3的射击效果能毁 伤敌人一门火炮,有1/6的射击效果能全部毁伤敌人火炮.
Monte Carlo 模拟
内容提纲
➢1.引言 ➢2.Monte Carlo模拟基本思想 ➢3.随机数生成函数 ➢4.应用实例举例 ➢5.排队论模拟 ➢6.Monte Carlo模拟求解规划问题
Monte Carlo方法:
引言(Introduction)
蒙特卡罗方法,又称随机模拟方法,属于计算数学的一个分支,它是在上世纪四 十年代中期为了适应当时原子能事业的发展而发展起来的。亦称统计模拟方法, statistical simulation method 利用随机数进行数值模拟的方法

Monte Carlo Simulations of the SU(2) Vacuum Structure

Monte Carlo Simulations of the SU(2) Vacuum Structure

a r X i v:h e p -l a t /9312002v 1 1 D e c 19931Monte Carlo Simulations of the SU(2)Vacuum StructureA.R.Levia ∗aCenter for Theoretical Physics,Laboratory for Nuclear Science and Department of Physics,Massachusetts Institute of Technology,Cambridge,Massachusetts 02139U.S.A.and Department of Physics,Boston University,590Commonwealth Ave.,Boston,Massachusetts 02215U.S.A.Lattice Monte Carlo simulations are performed for the SU(2)Yang Mills gauge theory in the presence of an Abelian background with external sources to obtain information on the effective potential.The goal is to investigate the lowest Landau mode that,in the continuum one-loop effective potential,is the crucial mode for instability.It is shown that also in the lattice formulation this lowest Landau mode plays a very peculiar role,and it is important for the understanding of the vacuum properties.1.INTRODUCTIONTo understand the IR behavior of non-Abelian gauge theory a non-perturbative framework is necessary.Therefore,lattice formulation is par-ticularly useful.However,the comparison be-tween one-loop perturbative expansion and lat-tice regularization might give important informa-tion.From this comparison we can learn about the trustworthiness of perturbative expansion.Furthermore,the loop expansion can provide in-dicative information for the lattice quantities.Despite the importance of Yang-Mills theories,the complete solution of non-Abelian gauge the-ories has yet to be found.In order to gain a bet-ter understanding of these theories,a necessary first step is the study of their vacuum structures.Nevertheless,the vacuum structure of such theo-ries has yet to be understood even in the simplest case,i.e.SU(2)without matter.Moreover,the infrared properties of such theories must be stud-ied systematically if we want to have some clue on confinement.Many authors have studied one-loop effective potential for SU(2)and the possible consequences [1,2].In addition,several Monte Carlo simula-tions have already been done with a wild range of techniques in 3and 4Dimensions[2,3].2Hδb 3(xδµ2−yδµ1).(2)2With this choice,the well known Savvidy result for the one-loop effective potential is obtainedE(H)=H248π2 ln gH2−i6πν(4)whereνare the eigenvalues of the second deriva-tive of the action with respect to the gaugefields, and the summation is over all the eigenvalues. This expression is a natural consequence of the saddle point approximation:V(H)=V classic−¯hδηδη+O(¯h2)(5)whereΩis the volume factor.The eigenvalue of this problem can be found by realizing that there is the same symmetry as the one of the Lan-dau levels problem,therefore we have exactly the same class of solutions.That yield to the eigen-valuesν=k2+(2n+1+2S z)gH(6) where k=k20+k2z,n=0,1,2,...and S z=±1is the gluon polarization.For the lowest landau mode,i.e.n=0,S z=−1,we haveν<0that give the imaginary part of the effective potential.Note that with Abelian background it is possible to obtain all eigenvalues positive only by insertion of non-gauge invariant terms in the TTICE RESULTSUntil now we have argued that this lowest Lan-dau mode plays a very particular role on the one loop perturbative expansion.This motivated us to understand what happened to this mode on lattice where the task is non-perturbative.To extract information on this mode on lattice we noted that,due to thefiniteness of the lat-tice k z,is quantized as well,with k z=2mπ/L z, where m is an integer.Therefore,the lowest in-homogeneous z-mode,m=1,becomes stable for lattices the extent of which in the z-direction is smaller thanL criticz=2πgH.(7)This enables us to search for the critical sizeL criticz.The homogeneous mode,m=0,which is always unstable,is eliminated by imposing a delta condition in the path integral.We perform Monte Carlo simulations that gen-erate a backgroundfield H in the z direction in the presence of an Abelian source of strength j. We use a heat bath updating procedure with periodic boundary conditions and for the com-putational technique we address to reference[2]. To eliminate the homogeneous mode m=0we force the Polyakov line in the z direction to take a fixed value different from zero.Monte Carlo sim-ulations are made with a lattice volume L3∗L z, where L is the size of the x,y,t directions.The finite effect due to L is not so crucial.In fact,for the observable that we are interested,it is suffi-cient to use L=12−16.Simulations are made by varying L z from4to50.The expectation value of the plaquette in the 1-2plane P[F12]as a function ofβand j is mea-sured.We monitor the quantityX=P[F12(β,0)]−P[F12(β,j)]3interested,these other contributions are smoothly variable functions,and thus they will not affect the critical behavior.We evaluated X for different values of βand j performing,for large j 4500sweeps after dis-charging 500for thermalization.For smaller j we increase the number of sweeps until a significant amount of datawas collected.Our data [2]show that there is no sign of the unstable mode away from the critical βregion (β=2.1−2.5).The situation changes dramat-ically in the critical region where the instability appears as a decrease of the vacuum energy con-tribution to the plaquette in the z direction.This effect becomes more evident in the presence of strong sources.The fact that the instability dis-appears outside of the scaling window is a strong evidence that the instability is a distinguishing feature of the continuum rather than the strong coupling vacuum.In order to study the IR prop-erty of the continuum theory we should remain in the region where the instability is manifest.The disappearance of the instability as β→0may give a clue to understanding the difference between the physics of the continuum and the strong coupling lattice theory.We systematically analyzed the critical region of βfor several values of j .The values of L critic z(β,j )are obtained by interpolating the kink of X and taking the median value.L critic zwas found to be dependent on the source j and on βin this region according to the renormal-ization group dependence.It is clear from our data that L critic zbelongs to the confinement phase and that there is good agreement with the renor-malization group equation.Our data follow the same dependence of the deconfinament transition [6]as should be for a dimensional scale length.Hence the ratio between the L critic and the de-confinament scale parameter L dec is independent of β.The relevant physical quantities must be obtained in the limit of vanishing of the induced field Q (j )→0.From our data we obtain L critic zL critic z (m )=T decm .(10)The simulations show that these modes followwith good approximation eq.(10).This is strong evidence for the harmonicity of this modes.This is a quite surprising result because we are in a re-gion of strong non-perturbative effects.It is stim-ulating to think in terms of a parallel between the situation described above and the integer Quan-tum Hall Effect.This similarity is based on the dual picture (z ↔t ,E ↔H ,etc...),and on the plateau structures for each mode.AcknowledgementsIt is a pleasure to thank Janos Polonyi and Suzhou Huang for several useful discussions.REFERENCES1.G.K.Savvidy,Phys.Lett.71B (1977)133;S.G.Matinyan and G.K.Savvidy Nucl.Phys.B134(1978)539;N.K.Nielsen and P.Olesen,Nucl.Phys.B144(1978)376;H.B.Nielsen and M.Ninomiya Nucl.Phys.B156(1979)1;J.Ambjørn and P.Olesen,Nucl.Phys.B170(1980)60and 265;J.Ambjørn,B.Felsager and P.Olesen,Nucl.Phys.B175(1980)349;L.Maiani,G.Martinelli,G.C.Rossi and M.Testa,Nucl.Phys.B273(1986)275.2. A.R.Levi and J.Polonyi,preprint CTP 2161.3.J.Ambjørn et al,Phys.Lett.245B (1990)575;P.Cea and L.Cosmai,Phys.Lett.249B (1990)114,264B (1991)415;Phys.Rev.D43(1991)620;preprint BARI-TH 129/93;H.D.Trot-tier and R.M.Woloshyn,Phys.Rev.Lett.70(1993)2053.4.L.F.Abbott,Nucl.Phys.B185(1981)189,and references therein.5.S.Huang and A.R.Levi,preprint BU-HEP-93-22.6.J.Kuti,J.Polonyi and K.Szlachanyi,Phys.Lett.98B (1981)199;E.Kovacs,Phys.Lett.118B (1982)125.。

动力学蒙特卡洛方法(KMC)及相关讨论

动力学蒙特卡洛方法(KMC)及相关讨论

动力学蒙特卡洛方法(KMC)及相关讨论动态模拟在目前的计算科学中占据着非常重要的位置。

随着计算能力和第一原理算法的发展,复杂的动态参数(扩散势垒、缺陷相互作用能等)均可利用第一原理计算得出。

因此,部分复杂的体系动态变化,如表面形貌演化或辐射损伤中缺陷集团的聚合-分解演变等,已可以较为精确的予以研究。

KMC——动力学蒙特卡洛方法(kinetic Monte Carlo)原理简单,适应性强,因此在很多情况下都是研究人员的首选。

此外,KMC在复杂体系或复杂过程中的算法发展也非常活跃。

本文试图介绍KMC方法的基础理论和若干进展。

KMC方法基本原理在原子模拟领域内,分子动力学(molecular dynamics, MD)具有突出的优势。

它可以非常精确的描述体系演化的轨迹。

一般情况下MD的时间步长在飞秒(s)量级,因此足以追踪原子振动的具体变化。

但是这一优势同时限制了MD在大时间尺度模拟上的应用。

现有的计算条件足以支持MD到10 ns,运用特殊的算法可以达到10 s的尺度。

即便如此,很多动态过程,如表面生长或材料老化等,时间跨度均在s 以上,大大超出了MD的应用范围。

有什么方法可以克服这种局限呢?当体系处于稳定状态时,我们可以将其描述为处于维势能函数面的一个局域极小值(阱底)处。

有限温度下,虽然体系内的原子不停的进行热运动,但是绝大部分时间内原子都是在势能阱底附近振动。

偶然情况下体系会越过不同势阱间的势垒从而完成一次“演化”,这类小概率事件才是决定体系演化的重点。

因此,如果我们将关注点从“原子”升格到“体系”,同时将“原子运动轨迹”粗化为“体系组态跃迁”,那么模拟的时间跨度就将从原子振动的尺度提高到组态跃迁的尺度。

这是因为这种处理方法摈弃了与体系穿越势垒无关的微小振动,而只着眼于体系的组态变化。

因此,虽然不能描绘原子的运动轨迹,但是作为体系演化,其“组态轨迹”仍然是正确的。

此外,因为组态变化的时间间隔很长,体系完成的连续两次演化是独立的,无记忆的,所以这个过程是一种典型的马尔可夫过程(Markov process),即体系从组态到组态,这一过程只与其跃迁速率有关。

MonteCarlo(蒙特卡洛法)简介

MonteCarlo(蒙特卡洛法)简介

一个例子 --

n=1000000; m=0; t=1; for i=1:n x=1; for k=1:7 ang=pi*rand; x=x+cos(ang); if x<0 l=0; t=0; end end if x>5 & t==1 l=1; else l=0; end m=m+l; t=1; end m/n
方差削减技术
对偶变量技术(适用正态分布函数) 取一组随机数Z_i,可得模拟值C_i ,i=1,2,..n 估计值为期平均C^ 再取Z_i 的对偶Z’_i=-Z_i,再生成估计值C’^ 然后去新的平均值C*=(C^+C’^)/2 则 varC*=1/2varC^+1/2cov(C^,C’^)< 1/2varC^+ 该技术使计算更稳定
随机数的取得
如果你对随机数有更高的要求,需要自己
编辑“随机数生成器” 最简单、最基本、最重要的一个概率分布 是(0,1)上的均匀分布(或称矩形分布) 例如在Matlab中,命令“rand()”将产生一 个(0,1)中均匀分布的随机数 你可以根据需要给随机数一个“种子”, 以求不同的数
基本思想和原理
基本思想:当所要求解的问题是某种事件出现
的概率,或者是某个随机变量的期望值时,它 们可以通过某种“试验”的方法,得到这种事 件出现的频率,或者这个随机变数的平均值, 并用它们作为问题的解。 原理:抓住事物运动的几何数量和几何特征, 利用数学方法来加以模拟,即进行一种数字模 拟实验。 它是以一个概率模型为基础,按照这个模型所 描绘的过程,通过模拟实验的结果,作为问题 的近似解。。
实现从已知概率分布抽样

Monte Carlo 方法资料

Monte Carlo 方法资料

Monte Carlo方法的基本思想
Monte Carlo 方法的基本思想是: 为了求解某个问题 , 建立一个恰 当的概率模型或随机过程 , 使得其参量(如事件的概率、随机变 量的数学期望等)等于所求问题的解 , 然后对模型或过程进行反 复多次的随机抽样试验 , 并对结果进行统计分析 , 最后计算所求 参量 , 得到问题的近似解。
③ 收敛速度与问题的维数无关 , 因此 , 较适用于求解多维问题。
④ 问题的求解过程取决于所构造的概率模型 , 而受问题条件限制的 影响较小 , 因此 , 对各种问题的适应性很强。
随机数的产生
1 随机数与伪随机数
Monte Carlo 方法的核心是随机抽样。 在该过程中往往需要各种各样分 布的随机变量其中最简单、最基本的是在[0 ,1]区间上均匀分布的 随机变量。 在该随机变量总体中抽取的子样 ξ 1 ,ξ 2 , … ,ξN 称为随 机数序列 , 其中每个个体称为随机数。 用数学的方法产生随机数是目前广泛使用的方法。 该方法的基本思想 是利用一种递推公式 :
"quantum" Monte Carlo: random walks are used to compute quantum-mechanical energies and wavefunctions, often to solve electronic structure problems, using Schrödinger’s equation as a formal starting point;
即当 N 充分大时 , 有 成立的概率等于1 , 亦即可以用 ξN 作为所求量 x 的估计值。
根据中心极限定理 , 如果随机变量 ξ的标准差 σ 不为零 , 那么 Monte Carlo 方法的误差ε为

蒙特卡罗(Monte Carlo)方法简介

蒙特卡罗(Monte Carlo)方法简介

蒙特卡罗(Monte Carlo)方法简介蒙特卡罗(Monte Carlo)方法简介蒙特卡罗(Monte Carlo)方法,也称为计算机随机模拟方法,是一种基于"随机数"的计算方法。

一起源这一方法源于美国在第二次世界大战进研制原子弹的"曼哈顿计划"。

Monte Carlo方法创始人主要是这四位:Stanislaw Marcin Ulam, Enrico Fermi, John von Neumann(学计算机的肯定都认识这个牛人吧)和Nicholas Metropolis。

Stanislaw Marcin Ulam是波兰裔美籍数学家,早年是研究拓扑的,后因参与曼哈顿工程,兴趣遂转向应用数学,他首先提出用Monte Carlo方法解决计算数学中的一些问题,然后又将其应用到解决链式反应的理论中去,可以说是MC方法的奠基人;Enrico Fermi是个物理大牛,理论和实验同时都是大牛,这在物理界很少见,在“物理大牛的八卦”那篇文章里提到这个人很多次,对于这么牛的人只能是英年早逝了(别说我嘴损啊,上帝都嫉妒!);John von Neumann可以说是计算机界的牛顿吧,太牛了,结果和Fermi一样,被上帝嫉妒了;Nicholas Metropolis,希腊裔美籍数学家,物理学家,计算机科学家,这个人对Monte Carlo方法做的贡献相当大,正式由于他提出的一种什么算法(名字忘了),才使得Monte Carlo方法能够得到如此广泛的应用,这人现在还活着,与前几位牛人不同,Metropolis很专一,他一生主要的贡献就是Monte Carlo方法。

蒙特卡罗方法的名字来源于摩纳哥的一个城市蒙地卡罗,该城市以赌博业闻名,而蒙特•罗方法正是以概率为基础的方法。

与它对应的是确定性算法。

二解决问题的基本思路Monte Carlo方法的基本思想很早以前就被人们所发现和利用。

早在17世纪,人们就知道用事件发生的"频率"来决定事件的"概率"。

MonteCarlo方法及其简单应用(图文)

MonteCarlo方法及其简单应用(图文)

MonteCarlo方法及其简单应用(图文)论文导读:本文介绍了Monte Carlo方法的思想,主要从在定积分计算方面介绍了随机投点法和平均值法,并将其推广到二重积分、三重积分和多重积分情形,最后以棋手分奖金问题介绍了Monte Carlo方法在古典概率问题中的应用.分析了误差,介绍了减少误差的方法. 给出这些方法的实例及其Mathematica实现程序.关键词:MonteCarlo方法,积分计算,古典概率,模拟1 引言Monte Carlo方法,源于第二次世界大战美国关于研制原子弹的“曼哈顿计划”.该计划的主持人之一、数学家冯·诺伊曼用驰名世界的赌城——摩纳哥的Monte Carlo——来命名这种方法,为它蒙上了一层神秘色彩.Monte Carlo方法的基本思想很早以前就被人们所发现和利用.19世纪人们用投针试验的方法来确定圆周率.20世纪40年代电子计算机的出现,特别是近年来高速电子计算机的出现,使得用数学方法在计算机上大量、快速地模拟这样的试验成为可能.Monte Carlo方法研究的问题大致可分为两种类型:一种是问题本身就是随机的,另一种本身属于确定性问题,但可以建立它的解与特定随机变量或随机过程的数字特征或分布函数之间的联系,因而也可用随机模拟方法解决.文[1]-[7] 介绍了Monte Carlo方法的思想,但没有给出具体的实例及实现过程。

发表论文。

本文介绍了MonteCarlo方法的思想,从计算定积分和古典概率两方面的应用进行研究,给出了实例及其Mathematica实现程序.2 Monte Carlo方法2.1 Monte Carlo方法思想概述Monte Carlo方法,有时也称随机模拟(RandomSimulation)方法或统计试验(Statistical Testing)方法.它的基本思想是:首先建立一个概率模型或随机过程,使它的参数等于问题的解;然后通过对模型或过程的观察、抽样来计算所求参数的统计特征;最后给出所求解的近似值,而解的精度可用估计值的标准误差来表示.假设所求的量是随机变量的数学期望,那么近似确定的方法是对进行重复抽样,产生相互独立的值的序列并计算其算术平均值:根据大数定理,当充分大时,以概率1成立,即可用作为的估计值.Monte Carlo方法以概率统计理论为基础,以随机抽样(随机变量的抽样)为手段,在很多方面有重要的应用.它的优点表现在三个方面:方法和程序的结构简单,易分析、易理解;收敛的概率性和收敛速度与问题的维数无关,很好的避免了维数问题;受问题条件限制的影响较小,很好的提高可行性.使用Monte Carlo方法的步骤如下:(l)构造或描述概率过程(2)实现从已知概率分布中抽样(3)建立各种估计量2.2 Monte Carlo方法的可行性从Monte Carlo方法的基本思想可以得到它通常的做法,利用数学或物理方法产生[0,1]中均匀分布的随机数,在变换得到任意分布的随机数.随机数个数很大时,可以由大数定理,求出事件的概率值.这种做法的可行性主要依据下面的事实:(1)如果随机变量的分布函数是,由于非降.对于任意的,(),可以定义:作为的反函数.我们考虑随机变量的分布,这里假定是连续函数,则对于有:(1)即服从上的均匀分布.(2)反之,如果服从上的均匀分布,则对于任意的分布函数,令,则:(2)因此是服从分布函数的随机变量.所以我们只要能够产生中均匀分布的随机变量的子样,那么通过(2)式我们就可以得到任意分布函数的随机变量的子样.再结合大数定理、就可以运用Monte Carlo方法进行随机模拟,解决一些实际的问题.3 Monte Carlo方法在定积分中的应用3.1随机投点法对于定积分.为使计算机模拟简单起见,设,有限,,令,并设是在上均匀分布的二维随机变量,其联合密度函数为.则是中曲线下方的面积(如图2).图2假设我们向中进行随机投点.若点落在下方(即)称为中的,否则称不中.则点中的概率为,若我们进行次投点,其中次中的.则可以得到的一个估计(3)该方法的具体计算步骤为:①独立地产生2个随机数,,i=1,&hellip;,n;②计算,,和;③统计的个数;④用(3)估计.例1 1777年,法国学者Buffon提出用试验方法求圆周率的值.原理如下:假设平面上有无数条距离为1的等距平行线,现向该平面随机地投掷一根长度为的针.则我们可以计算该针与任一平行线相交的概率.此处随机投针可以这样理解:针中心与最近的平行线间的距离x均匀地分布在区间上,针与平行线间的夹角(不管相交与否)均匀地分布在区间上(如图1).于是,针与线相交的充要条件是,从而针线相交概率为:图1而由大数定律可以估计出针线相交的概率,其中为掷针次数,为针线相交次数,从而圆周率.其mathematica实现语句见附录1.3.2 样本平均值法对积分,设是上的一个密度函数,改写(4)由矩法,若有个来自的观测值,则可给出的一个矩估计,这便是样本平均值法的基本原理.若,有限,可取.设是来自的随机数,则的一个估计为(5)该方法的具体计算步骤为:①独立地产生个随机数;②计算和,;③用(5)估计.后面将给出一个例子说明此方法的应用.4 Monte Carlo方法在计算多重积分中的应用方法一:(重积分)(7)其中为S维单位立方体,,在上有:.很明显.此时积分(5)可以看作为求维空间长方体V:的体积.即:(8)对于这种较为一般形式的多重积分计算问题,采用的还是随机投点.具体步骤如下:首先产生个随机数(i=1,2,&hellip;,)及,构造维随机向量,然后检验是否落后在V中,同理可以推论.检验是否成立,如果在构成的个随机向量中,有个随机向量落于V中,那么取作为积分的近似值,即,如果积分区域及被积函数不满足上述条件,那么可以通过变换便可达到所希望的条件.方法二:其中积分区域包含在维多面体中,此多面体决定于个不等式.设函数在内连续且满足条件:,是在维多面体中均匀分布的随机质点的个数,是在个随机点之中落入以维区域V为底以为顶之曲顶柱体内的随机点的个数.这里表示由不等式和决定的维多面体.则重积分的Monte Carlo近似计算公式为:=(9)例 2 在三维空间中,由三个圆柱面:,,围成一个立体,利用Monte Carlo方法求它的体积.分析:据题意,所求体积,其中{,,且,,}.记,,},考虑在空间内随机的产生个点,落在空间内有个,则.在Mathematica中模拟程序见附录2.5 在古典概率问题中的应用下面的例子说明了Monte Carlo方法在古典概率中的应用.例3 甲乙两位棋手棋艺相当,现他们在一项奖金为1000元的比赛中相遇,比赛为五局三胜制,已经进行了三局的比赛,结果为甲三胜一负,现因故要停止比赛,问应该如何分配这1000元比赛奖金才算公平?分析:平均分对甲欠公平,全归甲则对乙欠公平.合理的分法是按一定的比例分配.现在我们用计算机模拟两位棋手后面的比赛,是否就可以知道奖金分配方案.由于两位棋手的棋艺相当,可以假定他们在以下每局的比赛胜负的机会各半.Mathematica中函数产生随机数0或1,0与1出现的机会各占一半,可以用随机数1表示甲棋手胜,而随机数0表示乙胜.(也可以用中的随机实数来模拟两人的胜负,随机数大于0.5表示甲胜,否则乙胜)连续模拟1000次(或更多次数)每次模拟到甲乙两方乙有一方胜了三局为止.按所说方案分配奖金,1000次模拟结束后,计算两棋手每次的平均奖金,就是该棋手应得的奖金.模拟结果:甲:750,乙:250(程序见附录1)最终以甲分到;乙分到.即甲750元,乙250元.实际上,因为比赛只需进行两局.则可分出胜负.结果无非是以下四种情况之一:甲甲、甲乙、乙甲、乙乙.上面四种情况可看出,甲获胜的概率为,乙获胜的概率为.在Mathematica 中模拟程序见附录3.6 误差分析6.1 收敛性蒙特卡罗方法是由随机变量的简单子样的算术平均值:作为所求解的近似值.由大数定律可知,如独立同分布,且具有有限期望值(&lt;&infin;),则.即随机变量的简单子样的算术平均值,当子样数N充分大时,以概率1收敛于它的期望值.6.2 误差蒙特卡罗方法的近似值与真值的误差问题,概率论的中心极限定理给出了答案.该定理指出,如果随机变量序列,,&hellip;,独立同分布,且具有有限非零的方差,即是的分布密度函数.则当N充分大时,有如下的近似式其中称为置信度,1-称为置信水平.这表明,不等式近似地以概率1-成立,且误差收敛速度的阶为.通常,Monte Carlo方法的误差&epsilon;定义为上式中与置信度&alpha;是一一对应的,根据问题的要求确定出置信水平后,查标准正态分布表,就可以确定出.关于蒙特卡罗方法的误差需说明两点:第一,蒙特卡罗方法的误差为概率误差,这与其他数值计算方法是有区别的.第二,误差中的均方差是未知的,必须使用其估计值来代替,在计算所求量的同时,可计算出.例4 求用平均值法估计圆周率,并考虑置信度为5%,精度要求为0.01的情况下所需的试验次数.解:易知,故考虑令~,令,其期望值为,因此=,其中是[0,1]区间上均匀分布的随机数.此时,,,,所以(次).6.3 减小方差的各种技巧显然,当给定置信度&alpha;后,误差&epsilon;由&sigma;和N决定.要减小&epsilon;,或者是增大N,或者是减小方差.在固定的情况下,要把精度提高一个数量级,试验次数N 需增加两个数量级.因此,单纯增大N不是一个有效的办法.另一方面,如能减小估计的均方差&sigma;,比如降低一半,那误差就减小一半,这相当于N增大四倍的效果.因此降低方差的各种技巧,引起了人们的普遍注意.一般来说,降低方差的技巧,往往会使观察一个子样的时间增加.在固定时间内,使观察的样本数减少.所以,一种方法的优劣,需要由方差和观察一个子样的费用(使用计算机的时间)两者来衡量.这就是蒙特卡罗方法中效率的概念.它定义为,其中c 是观察一个子样的平均费用.显然越小,方法越有效.总的来说,增大样本的值对计算机要求较高;减小方差的技巧都只具有指导思想上的意义.对于实际的计算问题,往往要求对涉及的随机变量有先验的了解,或者对发生的物理过程的性态有一定的认识.通过利用这些预知的信息采取相应的手段减小误差,提高精度.附录1.(1)n=1000;p={}Do[m=0;Do[x=Random[];y=Random[];If[x+y&lt;=1,m++],{k,1,n}];AppendTo[p,N[4m/n]],{t,1,10}];Print[p];Sum[p[[t]],{t,1,10}]/10(2)n=10000;p={}Do[m=0;Do[x=Random[];y=Random[];If[x+y&lt;=1,m++],{k,1,n}];AppendTo[p,N[4m/n]],{t,1,10}];Print[p];Sum[p[[t]],{t,1,10}]/10(3)n=100000;p={}Do[m=0;Do[x=Random[];y=Random[];If[x+y&lt;=1,m++],{k,1,n}];AppendTo[p,N[4m/n]],{t,1,10}];Print[p];Sum[p[[t]],{t,1,10}]/102. n=1000;p={}Do[m=0;Do[x=Random[];y=Random[];z=Random[];If[x+y&lt;=1&amp;&amp;x+z&lt;=1&amp;&amp;y+z&lt;=1,m++],{k,1,n}]; AppendTo[p,N[8m/n]],{t,1,10}];Print[p];Sum[p[[t]],{t,1,10}]/103. n=1000;p={}Do[m=0;Do[x=Random[Integer]+2;y=Random[Integer]+1;If[x&gt;y,m++],{k,1,n}];AppendTo[p,N[m]],{t,1,20}]Print[m];{Sum[p[[t]],{t,1,20}]/20,1000-Sum[p[[t]],{t1,20}]/20}参考文献[1] 徐钟济.蒙特卡罗方法[M].上海:上海科学技术出版社,1985:171-188.[2] 茆诗松,王静龙,濮晓龙.高等数理统计[M].北京:高等教育出版社,2006:415-454.[3] 周铁,徐树方,张平文等.计算方法[M].吉林:清华大学出版社,2006:299-353.[4] 李尚志,陈发来,张韵华等.数学实验[M].北京:高等教育出版社,2004:23-30.[5] 王岩.Monte Carlo方法应用研究[J].云南大学学报(自然科学版),2006,28(S1): 23-26.[6] 薛毅,陈立萍.统计建模与R软件[M].北京:清华大学出版社,2008:476-485.[7] 杨自强.你也需要蒙特卡罗方法———提高应用水平的若干技巧[J]. 数理统计与管理, 2007,27(2):355-376.。

风险评估技术-蒙特卡罗模拟分析(Monte Carlo simulation)

风险评估技术-蒙特卡罗模拟分析(Monte Carlo simulation)

蒙特卡罗模拟分析(Monte Carlo simulation)1 概述很多系统过于复杂,无法运用分析技术对不确定性因素的影响进行模拟,但可以通过考虑投入随机变量和运行N次计算(即所谓模拟)的样本,以便获得希望结果的N个可能成果。

描述输入数据的不确定性并开展多项模拟(其中,对输入数据进行抽样以代表可能出现的结果)加以评估。

这种方法可以解决那些借助于分析方法很难理解和解决的复杂状况。

可以使用电子表格和其他常规工具进行系统开发,也可以使用更复杂的工具来满足一些更复杂的要求,很多要求所需的投资较少。

当该技术首次开发时,蒙特卡罗模拟所需的迭代过程缓慢,耗费时间。

但是,随着计算机技术的进步和理论的发展,例如latin-hypercube抽样法使很多应用程序的处理时间几乎变得微不足道。

2 用途蒙特卡罗模拟是评估不确定性因素在各种情况下对系统产生影响的方法。

这种方法通常用来评估各种可能结果的分布及值的频率,例如成本、周期、吞吐量、需求及类似的定量指标。

蒙特卡罗模拟法可以用于两种不同用途:●传统解析模型的不确定性的分布;●解析技术不能解决问题时进行概率计算。

3 输入输入到蒙特卡罗模拟法的是一个系统模型和关于输入类型的信息、不确定性源和期望的输出。

具有不确定性的输入数据被表示为具有一定分布的随机变量,根据不确定性的水平其分布具有或多或少的离散性。

为此,均匀分布、三角分布、正态分布和对数正态分布经常被使用。

4 过程过程如下:●确定尽可能准确代表所研究系统特性的模型或算法;●用随机数将模型运行多次,产生模型(系统模拟)输出。

在模拟不确定性效应的应用场合,模型以方程式的形式提供输入参数与输出之间的关系。

所选择的输入值取自这些参数中代表不确定性特点的适当的概率分布。

●在每一种情况下,计算机以不同的输入运行模型多次(经常到一万次)并产生多种输出。

这些输出可以用传统的统计方法进行处理,以提供均值、方差和置信区间等信息。

下面给出一个模拟例子。

Kinetic Monte Carlo

Kinetic Monte Carlo
i j∈n(i)
cicj + 2(qJq − µB H)
i
ci − N (qJq − µB H)
ห้องสมุดไป่ตู้
Ising Model
The canonical probability for a microstate is 1 −βH(s) µ(s) = e , Z with β = 1/kBT and the partition function Z=
Ising Model
So far we have discussed a Monte Carlo method for non-conserved order parameter (magnetization). For a conserved oder parameter (concentration) one uses Kawasaki dynamics: • choose a pair of (neighboring) spins, i.e., w0(s |s) = 1/2dN if we chose a spin at random and then a neighbor on a simple cubic lattice at random • exchange the spins subject to the Metropolis acceptance criterion This algorithm would suggest itself for the lattice gas interpretation. For surface diffusion one often works with non-conserved particle number mimicking desorbtion/adsorbtion events.

Monte Carlo Simulations of the Random-Field Ising Model

Monte Carlo Simulations of the Random-Field Ising Model

a r X i v :c o n d -m a t /0009228v 1 [c o n d -m a t .d i s -n n ] 14 S e p 2000Monte Carlo Simulations of the Random-Field Ising ModelW.C.Barber and D.P.BelangerDepartment of Physics,University of California,Santa Cruz,CA 95064,USAIsing Monte Carlo simulations of the random-field Ising system Fe 0.80Zn 0.20F 2are presented for H =10T.The specific heat critical behavior is consistent with α≈0and the staggered magnetization with β≈0.25±0.03.It has recently been shown experimentally [1]and through Monte Carlo (MC)simulations [2]that the three dimensional (d =3)dilute,anisotropic antiferromagnet Fe x Zn 1−x F 2in an applied uniform field exhibits the equilibrium critical behavior of the random-field Ising model (RFIM)[3]only for magnetic concentrations x >0.75.Although there is some agreement between previous MC and equilibrium experimental data,particularly the staggered susceptibility critical exponent,where γ=1.7±0.2for MC [4]and γ=1.58±0.08for experiment [1],and the correlation length critical exponent,where ν=1.1±0.2for MC [4]and ν=0.88±0.05for experiment [1],the experimental value [5]of the specific heat exponent,α=0.00±0.02,disagrees substantially with the simulation result [4]α=−0.5±0.2.The order parameter exponent,β=0.00±0.05from simulations [4],has yet to be measured experimentally.The MC exponents violate the Rushbrooke scaling inequality 2β+γ+α≥2,indicating possible inconsistencies.The previous MC studies employed finite scaling on ferromagnetic lattices of sizes L ×L ×L ,with L ≤16.Simulations were made over many random-field configurations and equilibrium was ensured by insisting that different starting configurations yielded consistent results at each temperature.In the present MC study,we have explored a much larger antiferromagnetic lattice and modeled our simulations closely after Fe 1−x Zn x F 2and the thermal cycling procedures used in experiments.Our large lattice size prevented the averaging over many random-field configurations,though the results obtained for a few different configurations yielded consistent results.We determined the staggered magnetization,M s ,and C m versus T .The resulting C m more closely mimics the symmetric,nearly logarithmic C m peak of the experiments [5]than the nondivergent cusp found in the earlier MC studies.In addition,we find β=0.25±0.03,in disagreement with the previous MC studies.The body-centered-tetragonal magnetic lattice of Fe 0.80Zn 0.20F 2is modeled as two cubic sub-lattices delineated as one-dimensional arrays bit coded to allow for a very large lattice size,2L ×L ×L with L =128,corresponding to 3.4×106spins.Periodic boundary conditions are imposed.The dilute MC Ising Hamiltonian is H =J 2 <ij>ǫi ǫj S i S j −h i ǫi S i (1)where S i =±2and ǫi =1if site i is occupied and zero if not,J 2=3.17K and h =8.73K.This approximates the experimental system which has one dominant Heisenberg exchange interaction,J 2,and a large single-ion anisotropy.The very small,frustrating interactions,J 1and J 3,are not included.The MC value for J 2is 0.60times that of FeF 2so that T c (0)corresponds roughly tothat of the experimental system.The value of h is chosen to have roughly the same effect as a field H =10T in the real system,as determined by the fields at which low temperature spin flips occur.This field also yields a shift T c (0)−T c (H )consistent with H =10T in the real system [6].Henceforth we refer to the applied uniform field as H =10T.For H =0,the sample is cooled to low temperatures in steps of 0.01K while magnetic sites are randomly visited an average of N times,where 2000<N <5000,and flipped with a probability given by the metropolis algorithm.Similarly,the sample is heated from an ordered state to above the transition.No hysteresis is observed at H =0using these two procedures.For H >0,two thermal cycling procedures are used to mimic those of the experiments.Each site is visited 5000<N <10000times per 0.01K temperature step.For zero-field cooling (ZFC),the sample is1started at H=0in a well ordered state(in experiments this state is prepared by cooling in H=0), thefield is subsequently applied,and the sample is heated through T c(H).Infield-cooling(FC),the sample is cooled with H applied.We observe no significant hysteresis with these two procedures, consistent with the experiments.M s and C m are calculated for each temperature.The simulations are performed on Linux/Gnu computers with CPU speeds between350to450MHz.Individual runs with L=128at5000MC steps per spin and covering10K in T typically take one week of CPU time.We have not seen any significant dependence on N in the range studied.Figure1shows C m versus T for L=128and N=5000for H=0and for ZFC and FC at H=10T.Each point represents a point-by-point derivative of the energy which is itself averaged over0.1K intervals.The behavior is very similar to that observed in birefringence and pulsed heat experiments[5].Namely,the H=0behavior exhibits the asymmetric cusp(α<0)characteristic of the random-exchange Ising model and,for H>0,a much more symmetric peak near T c(H) consistent with the symmetric,nearly logarithmic divergence.Previous MC studies employingfinite size scaling did not yield this behavior.Figure2shows the ZFC M s versus T−T c for L=128and N=5000for H=0and H=10T. Fits of the data for various ranges of|t|within10−3<|t|<10−1,yield the critical order-parameter exponentβ=0.35±0.04for H=0(REIM)andβ=0.25±0.03at H=10T(RFIM).The T c(H) values used in thefits were constrained by thefits to C m(H)To demonstrate that the results of the simulations are close to equilibrium,we did a simulation just below the transition for L=128,H=10T,and T=60.5.One lattice was started fully ordered,another fully ordered but with the sublattices reversed,and one randomly ordered.All three lattices converged to the same energy and M s within expectedfluctuations after5000steps per spin.Usingβ=0.25±0.03from MC andα=0.02±0.02andγ=1.58±0.08from experiments, we obtain2β+γ+α=2.08±0.14,which is consistent with the Rushbrooke scaling relation as an equality.Further studies will be needed to understand why the MC simulations with large an-tiferromagnetic lattices and thermal cycling yield different results than the earlier ferromagnetic MC simulations withfinite scaling analyses[4].For simulations of RFIM critical behavior employ-ing dilute antiferromagnets,the magnetic concentration should be kept well above the percolation threshold concentration for vacancies.This work has been supported by the Department of Energy Grant No.DE-FG03-87ER45324.FIG.1.C m vs.T for H=0and for ZFC(triangles)and FC(inverted triangles)at10T with L=128 and x=0.80.Note the symmetric peak for H=10T.The curves arefits to the data withα=−0.09and 0.00for the H=0and H=10T cases,respectively.FIG.2.M s vs.T−T c for H=0and ZFC at H=10T with L=128and x=0.80.The curves arefits to the data withβ=0.35and0.25for the H=0and H=10T cases,respectively.3。

有第二相粒子阻碍的晶粒粗化元胞自动机模拟

有第二相粒子阻碍的晶粒粗化元胞自动机模拟

有第二相粒子阻碍的晶粒粗化元胞自动机模拟范昌胜;郭强;刘泽照【摘要】采用元胞自动机算法模拟晶粒粗化过程中第二相粒子的阻碍现象.通过CA法,在考虑第二相粒子阻碍的晶粒粗化过程中模拟了其动力学、拓扑学及形态学的演化,并研究了温度及时间对粗化过程的影响.模拟结果显示:考虑第二相粒子,晶粒的粗化动力学指数接近3,而不是2;但是拓扑学特征与理想条件的粗化相同,即晶粒边数为6的晶粒占的比例最大,其次为五边形和七边形,而晶粒边数为3或10的晶粒所占比例很低,约为5%左右;CA法模拟晶粒粗化过程组织形态演化表明,随着保温时间的增加或温度的升高,晶粒平均尺寸在增大.模拟结果与相关文献中的结论相同,表明了本文CA模型的可靠性.【期刊名称】《西华大学学报(自然科学版)》【年(卷),期】2013(032)003【总页数】5页(P23-26,44)【关键词】元胞自动机算法;微观组织演化;晶粒粗化;各向异性【作者】范昌胜;郭强;刘泽照【作者单位】陕西工商职业学院工程管理系,陕西西安710119;西北工业大学理学院应用数学系,陕西西安710068;陕西工商职业学院工程管理系,陕西西安710119【正文语种】中文【中图分类】TG316.3晶粒尺寸是材料微观组织结构的一个重要指标,而材料的微观组织结构对其性能如塑性、韧性、强度、硬度和耐磨性等具有重大的影响[1]。

因此,对微观组织演化过程中晶粒粗化现象的研究在材料科学与工程领域一直占有举足轻重的位置,而且也是今后本领域的研究热点 [2-4]。

到目前为止,研究微观组织演化的常用手段包括实验研究、数值解析方法和微观组织演化的数值模拟法。

实验研究是一种常用的研究手段。

传统实验方法和解析手段尽管研究结果对实际生产起到了一定指导作用,但这种方法的缺点是工作量大,实验误差大[5]。

数值解析方法方法能够精确地描述微观组织的演化过程,而且取得了许多成果,但是计算工作量巨大[6]。

随着计算机技术的高速发展,传统的实验方法或解析手段已经不能满足现代材料科学技术发展的要求。

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Kinetic Monte Carlo simulations of heteroepitaxial growth with an atomistic model of elasticityPinku Nath,Madhav Ranganathan ⁎Department of Chemistry,Indian Institute of Technology Kanpur,Kanpur 208016,Indiaa b s t r a c ta r t i c l e i n f o Article history:Received 7February 2012Accepted 21May 2012Available online 28May 2012Keywords:Heteroepitaxial systemsKinetic Monte Carlo simulations Submonolayer growth Atomic elasticityWe have implemented Kinetic Monte Carlo (KMC)simulations of growth of heteroepitaxial thin films.A sim-ple cubic Solid-on-Solid (SOS)model is used to describe the atomic con figurations and nearest neighbor bonds are used to describe the energetics.Elastic effects are modeled using harmonic springs between atoms displaced from their lattice positions.The mis fit strain is a consequence of different equilibrium spring lengths for the substrate and film.The consistency of this elastic model with continuum theories for strained surfaces has been shown by performing elastic energy calculations for various morphologies.KMC simula-tions for submonolayer deposition show scaling behavior in the island size distribution.The resulting island shapes are predominantly square and do not show any shape transitions in the physically relevant range of conditions.This method gives a detailed understanding of elastic interactions and their interplay with surface diffusion in heteroepitaxial systems.©2012Elsevier B.V.All rights reserved.1.IntroductionStrained heteroepitaxial systems like Germanium (Ge)on Silicon (Si)are characterized by a small mismatch between the lattice parameters of the two materials,the film and the substrate.This mis fit can affect the morphology of these films.For example,it is well known that a growing heteroepitaxial film develops spontaneous undulations that eventually lead to formation of mounds and,sometimes,quantum dots [1–3].Controlled growth processes like Molecular Beam Epitaxy (MBE)can be used to closely study these phenomena and also grow high quality films and nanostructures [4–15].Heteroepitaxial systems have been of interest for nanoscale optoelectronic devices [16–18];e.g.Field-Effect-Transistors made with Si/Ge [19],GaAsP/InGaAs quantum wells for solar cells [20].The nature of growth of a strained film is in fluenced by the mis fit.For Ge on Si(100),the mis fit is about 4%.The first few layers of the film grow flat over the substrate.As more layers are deposited,the strain accumulated in the film increases and this causes spontaneous mound formation in the film (Fig.2).This type of growth is called Stranski –Krastanov growth [1,21]and is also found in other heteroepitaxial systems like InAs/GaAs [22].The transition from layer-by-layer growth to mound formation after a certain number of layers is referred to as the 2D/3D growth transition.For systems with larger mis fit,it is expected that this transition will be observed at lower coverages.It has been reported that the wetting layer in Ge/Si(100)is about 4–5ML thick [23]where it is about 1–2ML thick forInAs/GaAs [24].The shapes and size distribution of the mounds have also been a subject of many experimental [2,25,7]and theoretical stud-ies [26–31].The behavior during the early stages of mound formation,when the coverage is less than 1ML,has been investigated experimentally [32–34]and theoretically [35–42].During this stage the surface is mostly covered with monolayer islands of different sizes.The shape and size distribution of these islands depend on the surface studied.For example,in the case of Ge/Si(100),these islands are mostly rectan-gular in shape.The relative role of mis fit strain and anisotropic surface energy in the shape and size of these islands is a question of interest to us.KMC simulations have proved to be very useful for the study of surface morphology during growth [43].KMC allows us to study the interplay between deposition and surface diffusion during growth of a certain surface.For heteroepitaxial systems,the elastic strain modi fies the surface diffusion rates.These elastic effects are inherently long range effects.Due to this reason,the resulting KMC scheme is no longer a local scheme.Strain effects have been included in KMC simulations in many different ways.One class of approaches involves calculation of the elastic response of the semi-in finite substrate medium to the forces caused by the morphological features on the surface,like steps,adatoms,etc.The elastic response is typically calculated using continuum approaches involving Green's functions [41,44–46].Alternatively,the effect of elastic strain can be modeled using a reduction in the diffusion barrier for atoms in the film [37],or as an effective interaction between surface atoms in the film [40].In this article we focus on KMC simula-tions in which elastic effects are included through an atomistic model of elasticity with harmonic springs [47–50].Our method is closelySurface Science 606(2012)1450–1457⁎Corresponding author.Tel.:+915122596037;fax:+915122597436.E-mail address:madhavr@iitk.ac.in (M.Ranganathan).0039-6028/$–see front matter ©2012Elsevier B.V.All rights reserved.doi:10.1016/j.susc.2012.05.015Contents lists available at SciVerse ScienceDirectSurface Sciencej o u r na l h o me p a g e :ww w.e l s e v i e r.c o m /l o c a t e /s u s crelated to a prior work in this area,and we apply the method of calcu-lations on a realistic system.The elastic energy calculation involves a multidimensional minimization of the total energy of all the harmonic springs in the system.This makes the elastic computation extremely time-consuming.To implement this model in a KMC simulation,it is therefore necessary to come up with a set of approximations that will reduce the number of elasticity calculations.The rest of this article is organized as follows:In Section 2,we de-scribe the multicomponent Solid-on-Solid model that we use to de-scribe the surface.In Section 3,we show the KMC simulation method and the calculation of the surface hopping probabilities.In Section 4,we explain the model of elasticity used for the heteroepitaxial system.In Section 5,we illustrate how the KMC simulations can be performed with explicit elastic effects.In Section 6,we describe some results of our work,first on elastic energy calculations,and then submonolayer growth simulations.Finally,we make summarize the key findings and give our outlook on future work in this area in Section 7.2.Multicomponent Solid-on-Solid modelWe present here a lattice model for studying the evolution of the surface of a multicomponent system.Our model is based on the sim-ple cubic Solid-on-Solid (SOS)model,in which the crystal lattice is described by a 2D array of columns of various heights [51,43].For the single component SOS model,it is suf ficient to specify the heights of the columns in order to specify the surface.In the multicomponent case,each lattice site can be occupied by one of the various species as shown in Fig.1.In general,the behavior of the surface is dependent on the nature of the species in the columns.Hence it is essential in this model to have a variable to describe the type of the atom in each of the occupied lattice sites.Further,we impose a no-overhang condition,according to which a lattice site can only be occupied if the site one layer below is also occupied.The multicomponent SOS model can then be implemented in a Kinetic Monte Carlo (KMC)scheme in way similar to the standard SOS model using moves to describe the various processes of interest.The crystal is assumed to be growing in the +Z (upward)direction.The substrate is considered to in finitely thick and extend all the way to Z =−∞.We impose periodic boundary conditions in the X and Ydirections with the supercell dimension to be a multiple of the substrate lattice constant a s .Our simulations are designed to model the Ge on Si(100).We note that the systems we are considering do not have a simple cubic lattice,and the true Si(100)surface is reconstructed.The focus of this article is to study the interplay between elasticity and sur-face diffusion and we expect that the qualitative features of this behavior will be reproduced reasonably well in the simple cubic model.In the rest of this article,we will be using “Si ”and “Ge ”to refer to the substrate and film respectively.3.Kinetic Monte Carlo methodWe consider the dynamical processes taking place on the crystal surface during a typical MBE experiment,namely deposition and surface diffusion.We neglect bulk diffusion,defect formation and migration,and exchange of atoms in the bulk of the crystal.Surface diffusion is modeled by thermally activated hopping of surface atoms at random to their neighboring sites.The rate of this process can be written down in the Arrhenius form as r ¼ν0exp −E b =k B T ðÞð1Þwhere E b is energy barrier that has to be overcome in order for the atom to hop,k B is the Boltzmann constant,T is the absolute temperature and ν0is the attempt frequency.The attempt frequency ν0is independent of temperature and its value is around 1013s −1,corresponding to vibra-tional frequency of an atom in the lattice site.We assume the same value of ν0for all surface atoms in the system.The hopping rates satisfy the detailed balance condition,so thatr i →j ðÞ¼e −E j =k B T e −E i =k B ¼P eqj ðÞeq ð2Þwhere r (i →j )is the rate of transition from con figuration i to j involving a surface atom,and E i and E j are the energies of the two con figurations and P eq (i )and P eq (j )are their respective equilibriumprobabilities.Fig.1.Typical con figuration in a two-component simple cubic SOS model (left)and a cross-sectional view of the rectangular section (right).The black and white balls represent different atoms.For example,the black atoms can be Si atoms and the white atoms can beGe.Fig.2.A cartoon showing how strain caused by mismatch between the film (gray circles)and the substrate (black circles)is signi ficantly relaxed at surfaces,and at the edges of steps andmounds.Fig.3.The elastic model showing springs between NN and NNN atoms for a typical con-figuration.The springs in the film (gray)have different parameters than in the substrate (black).In a typical optimization to calculate the elastic energy,we consider numerically op-timize displacements in the top few layers of the substrate (two layers in the above figure),and solve for the displacement field analytically in the rest of the semi-in finite substrate;as indicated by the wavy lines.1451P.Nath,M.Ranganathan /Surface Science 606(2012)1450–1457Detailed balance ensures that when there is no deposition,the system will tend towards equilibrium with a Boltzmann distribution of energy.In the simulation of this model,we carry out a combination of random deposi-tion and hopping moves with the appropriate acceptance rates.The barrier energy E b is the total energy that a surface atom needs to have in order to hop to a neighboring site.In the absence of elastic energy,this barrier is the energy required to break all the bonds of the atom.The value of this barrier depends on the number and strength of these bonds,so E b =E bond =∑j γj where γi is the strength of the bond between the atom (which we attempt to hop)and the i th atom.The sum is performed over all the bonds of the surface atom.The strength of the bond depends on the particular model of interac-tion chosen.For example,if we are describing a Ge atom on the (100)surface of Si using our simple cubic lattice,we use a nearest neighbor (NN)interaction model consisting of two kinds of bonds with energies E h and E v corresponding to bonds in the horizontal and vertical direc-tions respectively.In the absence of elastic energy,the barrier energy is a local quantity which can be stored for all surface atoms.After every move,the barrier energy of a small number of atoms has to be updated.This makes it possible to implement the KMC using a rejection-free method similar to the Bortz –Kalos –Lebowitz algorithm [52].This is not possible when we include elastic effects.Hence,we implement a KMC scheme with ac-ceptance and rejection of atom hopping moves so as to satisfy Eq.(1).4.Elastic modelThe lattice mismatch of a heteroepitaxial film is de fined as =(a f −a s )/a s where a f and a s represent the lattice constants of film and the substrate respectively.For a film of Ge on Si,this value is around 4%.This mismatch can be controlled by depositing a mixture of Si and Ge on the film of varying concentrations.Following previous work [53,54],we implement a model of hetero-epitaxy using springs between atoms.This model is explained in the references but we give a brief review here.Associated with each atom is a 3-dimensional elastic displacement from its lattice position.Each atom is connected by springs to its nearest neighbors (NN)and next nearest neighbors (NNN)(Fig.3).The energy of each spring is given by E spring =(1/2)k (d −l eq )2where d is the distance between the atoms and l eq is the equilibrium length at which the spring energy is zero and k is the spring constant.The distance d between atoms on neigh-boring lattice sites is calculated geometrically using the lattice constants and the elastic displacements.The values of k and l eq depend on the nature of the bond (NN or NNN),and the species of atoms forming the bond.For a pure cubic system,the two independent elastic moduli,C 11and C 12,are related to the spring constants by the relations [55]k L ¼a C 11−2C 12ðÞk D ¼aC 12ð3Þwhere k L and k D are the spring constants for the NN and NNN bonds re-spectively,and a is the equilibrium length of the lateral springs in the simple cubic lattice model.The elastic energy leads to a restoring force in the system that tries to minimize the spring energy of each atom.In order to model heteroepitaxial systems,we assign different equilibrium spring lengths to the film and the substrate atoms corresponding to their lattice con-stants.The equilibrium bond lengths of the NN and the NNN bonds in the film are a f and a f ffiffiffi2p .We assumed that the spring constants k L and k D remain the same for different kinds of atoms,and that the equilibrium length of a bond between the substrate atom and the film atom is equal to that of a bond between two film atoms.We will discuss the implica-tions of these assumptions later.The cell periodicity ensures that for a pure substrate,there are no elas-tic effects.When a single complete monolayer is deposited on the sub-strate,the bonds are compressed in the horizontal (X and Y)directionsas shown in Fig.3.This causes the film to expand in the Z-direction.The equilibrium location of the atoms in the Z direction can be calculated using the minimum energy or zero force condition.Under this condition,the separation of atoms in the Z-direction,a L can be obtained by solving k L a L −a s ðÞþ4k D ðffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffia 2Lþa 2s q −a f ffiffiffi2p Þa L ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffia 2L þa 2sq ¼0:ð4ÞThe elastic energy is a function of the displacements of all the atoms.By minimizing this function with respect to the displacements,we cal-culate the optimum displacements.This is the elastic relaxation that has to be carried out at every step of the Kinetic Monte Carlo simulation.We can do this using any of the standard multidimensional minimization routines like conjugate gradient,steepest descent,etc.We are interested in growth of a thin film on a large substrate.For a semi-in finite substrate with given displacements of the topmost layer,the elastic displacements of the substrate are calculated analytically based on the procedure outlined in Ref.[47].In this procedure,we line-arize the springs in the substrate and expand the displacement vector field {U }in a Fourier basis in the X and Y directions.This Fourier trans-form,~U n o decays exponentially inside the substrate.We expand ~U n o using eigenvalues and eigenvectors and the coef ficients are calculatedusing the displacements of the topmost layer of the substrate.The dis-placement field {U }is used to calculate the contribution to the elastic energy from the substrate.This procedure allows us to greatly reduce the number of atoms involved in the elastic energy calculation.In practice,we treat a few layers of the substrate as discrete layers before treating the remaining as a continuum as shown in Fig.3.Since we treat a few layers of the substrate as discrete layers,we can potentially model intermixing wherein atoms from the substrate migrate into the film.This intermixing is believed to be one of the mechanisms for strain relaxation in these systems.We end this section with a few comments about the elastic model and the KMC simulations.Firstly,our KMC simulations are implemented on a Simple cubic SOS model.We allow for atoms to hop from one site to one of their neighboring sites.The elastic displacements are localized on the lattice sites.These displacements are used in the calculation of the distance between two atoms on neighboring lattice sites.It is,therefore,implicit that these displacements are not too large (not greater than half the spacing between atoms).Secondly,there are some parameters in the model like the spring constants and the equilibrium lengths for all the different types of bonds.The equilibrium lengths of bonds involving only one kind of atom are based on the bulk lattice constants of the atom.But there is some arbitrariness in the choice of the equilibrium length for a bond involving two different atoms.The spring constants of bonds involving only the film or the substrate atoms are chosen so as to reproduce the bulk elastic moduli for these systems.ThereisFig.4.The interaction energy between the ridge and trench features of the inset for D =4plotted as a function of d .The dots show the results of our model and the solid curve is f (d )of Eq.(5)multiplied by 2.2.1452P.Nath,M.Ranganathan /Surface Science 606(2012)1450–1457again some arbitrariness in the choice of the spring constant for a bond involving two different atoms.In particular,the choices of these param-eters influence the wetting layer and the2D/3D growth transition.Fur-ther,real Si or Ge is neither cubic,nor isotropic.In fact the diffusion of Ge atoms on Si(100)surface has been shown to be strongly anisotropic because of the2×1reconstruction.Due to the above reasons,we feel that our model can be used to study the qualitative behavior,but care should be exercised while making quantitative comparisons to other models and experiments.Finally,we note that for an individual atom, the elastic energy is typically a very small part of the entire barrier energy in the KMC simulations discussed in the next section.Despite this,it is be-lieved to play an important role in the structures formed during growth.5.KMC simulations with elasticityThe KMC method we have described in Section3is implemented using the hopping rate given by Eq.(1).The barrier energy is treated as a sum of the energy due to the bonds and the elastic energy due to the displacements.The displacements are calculated for afixed config-uration of atoms.Implicit in this method is the assumption that the elas-tic relaxation of the system takes place on much timescale smaller than that of surface diffusion.The elastic energy causes a reduction of the barrier for surface diffusion,since the springs in our model cannot have energies less than zero.Thus we can write E b=E bond−E el,where E el is the elastic energy contribution to the hopping barrier.To calculate E el,we need to calculate the difference of the total elastic energy of the system with and without the atom.Each elastic energy calculation needs a minimization of the energy function to calculate the atomic dis-placements.The elastic energy computation is the most expensive part of the entire calculation.We therefore consider some approximate schemes that will minimize the number of these calculations.Firstly we assume that the dominant contribution to the elastic en-ergy comes from the elastic springs of the atom to its NN and NNN atoms.Thus we approximate E el as E el¼∑bonds12k bond l bond−l eq bondÀÁ2 where k bond is the spring constant of the bond,l bond is the distance be-tween the atoms forming the bond,and l bondeq is the equilibrium bond length.This approximation allows us to calculate the elastic energy of the atom from its configuration without having to consider the elastic energy of the system without the atom.This reduces the total number of elastic energy calculations by a factor of2.In order to construct more approximations,we need to use some information about the elastic energy.Let us consider an adatom,i.e.a surface atom that is only bonded to one NN and4NNN atoms.Since the bond energy is only due to NN atoms,the total bond energy of an adatom is equal to E v.For an arbitrary surface,the elastic energy of an adatom depends on the detailed configuration of the surface.If the surface is notflat, the energy of adatoms located at different points on the surface will be different.As we shall show in Section1,this difference in elastic energy of adatoms on a given layer is negligible compared to the elastic energy of other surface atoms.We neglect this difference in our KMC simulations.This greatly simplifies the elastic energy calculation for adatoms.The elastic energy plays an important role in the diffusion of other surface atoms and has to be calculated accurately for those atoms.Since a significant fraction of atomic moves on the surface in-volve adatoms,our approximation leads to a significant time-saving. Further,since there are very few atoms that have fewer bonds than adatoms,we will set the time unit in our KMC simulations as the time for one adatom hop.With this approximation,we can write the hopping rate r as r=r ad exp−(E b−E b ad/k B T)where r ad is the hopping rate of an adatom and E b ad is the total barrier energy for an adatom.We choose our timestep so that r ad=1,thereby eliminating rejection of moves in-volving adatoms.This makes it possible to extend our simulations to longer times and study the growth of multiple layers.A similar approx-imation has been used recently in Ref.[50].We mention one more detail about the adatom energies here.The energy of afilm adatom on aflat substrate depends on the bond energy and the elastic interaction parameters involving thefilm and the sub-strate atoms.As we had mentioned earlier,there is someflexibility as regards the various parameters for a bond involving two different atoms.If we assume that the elastic parameters and the bonds are iden-tical for the interface layer and thefilm,then wefind that adatoms from different layers hop at the same rate.Any other choice will lead to an adatom hopping frequency that is dependent on the number of layers. Such dependence plays a role in the transition from2D layer-by-layer growth to3D mound growth.For instance,slower diffusion of adatoms in the upper layers will favor3D growth.A layer dependent adatom energy(elastic or bond)corresponds to a wetting layer in our system.A more careful examination of the wetting layer and the2D/3D transition will be the subject of a subsequent article.6.Results and discussionWe divide our results into two sections.In thefirst,we discuss elasticity calculations for various configurations.These calculations validate the model,and help us in constructing the approximations that were discussed in Section5.In the second,we discuss KMC sim-ulations of submonolayer growth.All calculations were carried out on a128×128system.The various parameters were chosen to mimic the Ge on Si(100).The lattice spacing in Si is2.715Åand that in Ge is 2.832Å.We performed Fourier transforms using the Fast Fourier Transform routine[56].The eigenvalues and eigenvectors for the substrate contribution were calculated using the singular value de-composition routine in LAPACK[57].The multidimensional minimi-zation of the elastic energy to calculate the elastic displacements was accomplished using the conjugate gradient optimizer in the GNU Scientific Library[58].The spring constants are taken as k L=73eV/nm2and k D=96eV/nm2to approximately reproduce the elastic moduli of Si.We have used the same spring constants for all pairs of atoms.The equilibrium bond length of a bond involving an Si and a Ge atom was taken to be equal to that of a Ge bond.To compare the submonolayer growth calculations with earlier work,we have used a temperature of500K,E v=0.8eV and E h=0.3eV.We have used the same value of E v for Ge atoms on bare Si(100)and Ge atoms atop Ge islands.The surface diffusion coefficient D s can be estimated as D s=(1/4)ν0exp(−E v/k B T)a s2which works out to about2×104s−1in length units of a s.Since we ignored all the anisotropic effects in the hop-ping barriers,we do not have any anisotropy in the surface diffusion constant.Besides the Si–Ge model system(4%misfit),we have per-formed calculations for systems with no misfit and8%misfit.Though we do not show this result here,we have verified that3D island forma-tion was observed at low coverages(b0.5ML)when we used8%misfit and spring constants larger by a factor of10.Each KMC simulation tra-jectory was generated on a64-bit Intel X5500series processor;and120 parallel trajectories were simulated to get good statistics.A simulation of1ML on these systems took about144h;but the simulation time in-creases for subsequent layers because of the increase in the total num-ber offilm atoms.6.1.Elastic energy calculationsIt has been proposed that the elastic interaction energy between two adatoms a distance r apart on aflat epitaxial layer is proportional to1/r3. This interaction is due to force dipoles located near each of the adatoms. On the other hand,steps on strainedfilms cause force monopoles and the interaction between steps distance r apart is proportional to log(r).For a periodic array of steps,the dependence is slightly modified.We test this dependence by performing elastic calculations for the configuration shown with d and D in Fig.4.We calculated the elastic energies of a flat surface E f,a surface with a ridge E r,a surface with a trench E t and a surface with both a ridge and a trench E rt.The elastic energy1453P.Nath,M.Ranganathan/Surface Science606(2012)1450–1457of interaction between the ridge and the trench features U int is given by U int =E rt −E r −E t +E f .According to continuum elasticity theory,this interaction energy is proportional tof d ðÞ¼−logsin 2πd þD ðÞ=L ðÞ"#ð5Þwhere L is the periodicity of the system.The constant of proportionality is related to the magnitude of the force monopoles at the step edges.We show that our interaction energy can be fitted to Eq.(5)with a single fitting parameter.The difference at smaller distances is simply a consequence of the difference between the continuum model and our lattice model at short length scales.In general,we expect to see some differences for moder-ate length scales,so it is remarkable that the graph in Fig.4agrees so well at moderate length scales.It is possibly due to the symmetric na-ture of the con figurations considered.Analogous to the usual thermodynamic chemical potential,we de fine an elastic chemical potential μel of an adatom on a given surface as the dif-ference in the elastic energy of the system with and without the adatom.For a given con figuration of a surface,μel changes for different locations asshown in Fig.5.The figure illustrates that the elastic contribution to the hopping barrier is largest at the island edges and is almost constant along a flat surface.Thus the atoms at the edges of islands have a reduced hopping barrier due to elasticity.The adatoms on top of the island have a smaller elastic energy than those on the bare substrate.The reduction in elastic energy causes slower diffusion of adatoms on top of islands.These two effects eventually lead to a transition from layer-by-layer growth to 3D mound formation in the KMC simulations.As a final demonstration of our elastic model,we show the dependence of μel at the island edges on the size of the island in Fig.6.The increase in island energy with island size follows a trend consistent with the continuum elasticity based calcu-lations of Refs.[41,37].As the size of the islands becomes large,the elastic energy increases much more slowly.We note that the chemical potential as calculated by us is signi ficantly smaller than that calculated in Ref.[41].This difference will be re flected again in the results of the KMC simula-tions in the next section.6.2.Submonolayer growthWhen Ge atoms are deposited on an atomically flat Si substrate,the deposited atoms diffuse on the surface and,occasionally,get attached to Ge islands on the surface.The resulting surface shows many islands typ-ically of different sizes as shown in Fig.7for different values of the mis-match.In Fig.7(d),we show a snapshot of a system with 4%mismatch,but a spring constant 5times larger.In this case,we can see some mul-tilayer islands too.If the deposition rate is slow,then the adatoms have suf ficient time to get attached to existing islands and the average size of islands is large.The island size distribution therefore depends on the ratio of the surface diffusion coef ficient to the flux,i.e.the D s /F ratio.We performed KMC simulations of submonolayer growth of Ge on Si.By choosing F =0.1ML/s and F =10ML/s,we get D s /F ratios of 2×105and 2×103(in units of a s 2)respectively.Following the previous section,we take a constant elastic energy for adatoms on any given layer.KMC simulations of submonolayer growth have been performed earlier using continuum elasticity model [41]and using an effective adatom interaction model [40].In both these cases the island size distribution for a given D s /F ratio was consistent with the scaling form [40,38]N s θðÞb s >2θ¼f s =b s >ðÞð6Þwhere N s represent number of islands of size s (s ≥2)when the surface coverage is θand the average island size is b s >.This form was derived for irreversible submonolayer growth conditions in the absence of elas-tic interactions.In Fig.8,we show the scaled island size distribution as a function of s /b s >for θ=0.1−0.29for F =0.1ML/s.The points seem to fit on a master curve indicating that there is very little mound formation.Chemical potential (meV)b)a)102030Elastic Displacementsc)Fig.5.(a)Graph showing μel of an adatom located at various distances on the surface along the line AB.The inset figure shows part of the surface con figuration with the line AB.(b)A contour map of μel on a surface with a 9×9island,and (c)cross-section of the surface showing the elastic displacement of atoms (magni fied by factor 10for better visibility)in the vicinity of an island.102030400200400600µe l (m e V )sFig.6.μel for a Ge adatom next to a square Ge island of size s .1454P.Nath,M.Ranganathan /Surface Science 606(2012)1450–1457。

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