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模板:Statistical mechanics

In mathematical physics, especially as introduced into statistical mechanics and thermodynamics by J. Willard Gibbs in 1902, an ensemble (also statistical ensemble) is an idealization consisting of a large number of virtual copies (sometimes infinitely many) of a system, considered all at once, each of which represents a possible state that the real system might be in. In other words, a statistical ensemble is a probability distribution for the state of the system.[1]

在数学物理学中,正则系综由 J·威廉·吉布森(J. Willard Gibbs) 在1902年引入到统计力学和热力学中,它是一个体系的大量可能状态的集合(有时是无限多的)。换句话说,系综是系统状态的概率分布。[1]

A thermodynamic ensemble is a specific variety of statistical ensemble that, among other properties, is in statistical equilibrium (defined below), and is used to derive the properties of thermodynamic systems from the laws of classical or quantum mechanics.[2][3]



The ensemble formalises the notion that an experimenter repeating an experiment again and again under the same macroscopic conditions, but unable to control the microscopic details, may expect to observe a range of different outcomes.


The notional size of ensembles in thermodynamics, statistical mechanics and quantum statistical mechanics can be very large, including every possible microscopic state the system could be in, consistent with its observed macroscopic properties. For many important physical cases, it is possible to calculate averages directly over the whole of the thermodynamic ensemble, to obtain explicit formulas for many of the thermodynamic quantities of interest, often in terms of the appropriate partition function.


The concept of an equilibrium or stationary ensemble is crucial to many applications of statistical ensembles. Although a mechanical system certainly evolves over time, the ensemble does not necessarily have to evolve. In fact, the ensemble will not evolve if it contains all past and future phases of the system. Such a statistical ensemble, one that does not change over time, is called stationary and can be said to be in statistical equilibrium.[1]



The word "ensemble" is also used for a smaller set of possibilities sampled from the full set of possible states. For example, a collection of walkers in a Markov chain Monte Carlo iteration is called an ensemble in some of the literature.


The term "ensemble" is often used in physics and the physics-influenced literature. In probability theory, the term probability space is more prevalent.



文件:Statistical Ensembles.png
Visual representation of five statistical ensembles.

Visual representation of five statistical ensembles.


The study of thermodynamics is concerned with systems that appear to human perception to be "static" (despite the motion of their internal parts), and which can be described simply by a set of macroscopically observable variables. These systems can be described by statistical ensembles that depend on a few observable parameters, and which are in statistical equilibrium. Gibbs noted that different macroscopic constraints lead to different types of ensembles, with particular statistical characteristics. Three important thermodynamic ensembles were defined by Gibbs:[1]

热力学研究所涉及的系统在人类看来是”静态的”(尽管它们的内部是在运动着的) ,并且它可以被一组宏观上可观察到的变量简单地描述出来。这些系统可以用依赖于几个可观测参数的统计系综来描述,并且这些参数处于统计均衡状态。吉布斯指出,不同的宏观上的制约会导致具有特定的统计特征不同类型的系综。吉布斯定义了三个重要的热力学系综:[1]

  • Microcanonical ensemble or NVE ensemble—a statistical ensemble where the total energy of the system and the number of particles in the system are each fixed to particular values; each of the members of the ensemble are required to have the same total energy and particle number. The system must remain totally isolated (unable to exchange energy or particles with its environment) in order to stay in statistical equilibrium.[1]
  • 微正则系综或 NVE 系综——一种统计系综,其中系统的总能量和系统中的粒子数量均固定为特定值;集合中的每个成员都必须具有相同的总能量和粒子数。为了保持统计平衡,系统必须保持完全独立(无法与其环境交换能量或粒子)。[1]
  • Canonical ensemble or NVT ensemble—a statistical ensemble where the energy is not known exactly but the number of particles is fixed. In place of the energy, the temperature is specified. The canonical ensemble is appropriate for describing a closed system which is in, or has been in, weak thermal contact with a heat bath. In order to be in statistical equilibrium, the system must remain totally closed (unable to exchange particles with its environment) and may come into weak thermal contact with other systems that are described by ensembles with the same temperature.[1]
  • 正则系综或 NVT 系综是一种能量未知但粒子数量是固定的系综。这里的能量不固定,但温度是固定的。正则系综适用于描述与热浴处于或已经处于弱热接触的封闭系统。为了达到统计平衡,系统必须保持完全封闭(无法与其环境交换粒子),并且可能与具有相同温度的系综发生弱热接触。[1]
  • Grand canonical ensemble or μVT ensemble—a statistical ensemble where neither the energy nor particle number are fixed. In their place, the temperature and chemical potential are specified. The grand canonical ensemble is appropriate for describing an open system: one which is in, or has been in, weak contact with a reservoir (thermal contact, chemical contact, radiative contact, electrical contact, etc.). The ensemble remains in statistical equilibrium if the system comes into weak contact with other systems that are described by ensembles with the same temperature and chemical potential.[1]
  • 巨正则系综或 μVT 系综是一种能量和粒子数都不固定的统计系综。这里的温度和化学势是固定的。巨正则系综适用于描述开放系统:与储存池(一个热容量很大的环境,传递多少能量温度都不变)处于或已经处于弱接触(热接触、化学接触、辐射接触、电接触等)的系统。对于巨正则系综来说,系统与具有相同温度和化学势的其他系统发生弱接触,巨正则系综将仍然保持在统计平衡中。

The calculations that can be made using each of these ensembles are explored further in their respective articles.


Other thermodynamic ensembles can be also defined, corresponding to different physical requirements, for which analogous formulae can often similarly be derived.



The precise mathematical expression for a statistical ensemble has a distinct form depending on the type of mechanics under consideration (quantum or classical). In the classical case, the ensemble is a probability distribution over the microstates. In quantum mechanics, this notion, due to von Neumann, is a way of assigning a probability distribution over the results of each complete set of commuting observables.


In classical mechanics, the ensemble is instead written as a probability distribution in phase space; the microstates are the result of partitioning phase space into equal-sized units, although the size of these units can be chosen somewhat arbitrarily.

在经典力学中,系综是相空间中的概率分布; 微观状态是将相空间划分为相同大小的单元的结果,这些单元的大小可以被任意选择。


Putting aside for the moment the question of how statistical ensembles are generated operationally, we should be able to perform the following two operations on ensembles A, B of the same system:

不论我们如何生成统计系综,我们都应该能对同一系统的系综 A、B 进行以下两个操作:

  • Test whether A, B are statistically equivalent.
  • If p is a real number such that 0 < p < 1, then produce a new ensemble by probabilistic sampling from A with probability p and from B with probability 1 – p.
  • 测试系综A,B是否统计相等。
  • 如果 p 是一个满足0<p<1的实数,那么可以通过概率采样产生一个新的系综,它在A中的概率是p,在B中的概率是1-p。

Under certain conditions, therefore, equivalence classes of statistical ensembles have the structure of a convex set.



A statistical ensemble in quantum mechanics (also known as a mixed state) is most often represented by a density matrix, denoted by [math]\displaystyle{ \hat{\rho} }[/math]. The density matrix provides a fully general tool that can incorporate both quantum uncertainties (present even if the state of the system were completely known) and classical uncertainties (due to a lack of knowledge) in a unified manner. Any physical observable X in quantum mechanics can be written as an operator, . The expectation value of this operator on the statistical ensemble [math]\displaystyle{ \rho }[/math] is given by the following trace:

系综在量子力学(也称为混合状态)最常以密度矩阵来表示, [math]\displaystyle{ \hat{\rho} }[/math]. 。密度矩阵提供了一个完全通用的方式,它可以兼顾量子不确定性(即使系统的状态完全已知)和经典不确定性(由于缺乏知识而导致)。任何可被观察到物理量 X 在量子力学中都可以被写成算子, 。这个算子在统计系综 [math]\displaystyle{ \rho }[/math] 的期望值由下面的迹所约束:

[math]\displaystyle{ \langle X \rangle = \operatorname{Tr}(\hat X \rho). }[/math]

This can be used to evaluate averages (operator ), variances (using operator 2), covariances (using operator X̂Ŷ), etc. The density matrix must always have a trace of 1: [math]\displaystyle{ \operatorname{Tr}{\hat{\rho}}=1 }[/math] (this essentially is the condition that the probabilities must add up to one).

这可以用来计算平均值(算子) ,方差(算子 2) ,协方差(算子X̂Ŷ)等。密度矩阵必须始终满足一个迹,1: [math]\displaystyle{ \operatorname{Tr}{\hat{\rho}}=1 }[/math](这本质上是使得概率加起来必须等于1的条件)。

In general, the ensemble evolves over time according to the von Neumann equation.

一般来说,根据冯 · 诺依曼方程,这个系综会随着时间而演化。

Equilibrium ensembles (those that do not evolve over time, [math]\displaystyle{ d\hat{\rho}/dt=0 }[/math]) can be written solely as a function of conserved variables. For example, the microcanonical ensemble and canonical ensemble are strictly functions of the total energy, which is measured by the total energy operator Ĥ (Hamiltonian). The grand canonical ensemble is additionally a function of the particle number, measured by the total particle number operator . Such equilibrium ensembles are a diagonal matrix in the orthogonal basis of states that simultaneously diagonalize each conserved variable. In bra–ket notation, the density matrix is

平衡系综(那些不随时间演化的, [math]\displaystyle{ d\hat{\rho}/dt=0 }[/math])可以单独写成守恒变量的函数。例如,微正则系综和正则系综是总能量的函数,它由总能量算子(哈密顿量)表示。此外,巨正则系综是粒子数的函数,由粒子总数算子表示。这样的平衡系综在同时对角化每个守恒的变量的基中是对角矩阵。用狄拉克符号表示,密度矩阵为

[math]\displaystyle{ \hat \rho = \sum_i P_i |\psi_i\rangle \langle \psi_i | }[/math]

where the |ψi, indexed by i, are the elements of a complete and orthogonal basis. (Note that in other bases, the density matrix is not necessarily diagonal.)



文件:Hamiltonian flow classical.gif
Evolution of an ensemble of classical systems in phase space (top). Each system consists of one massive particle in a one-dimensional potential well (red curve, lower figure). The initially compact ensemble becomes swirled up over time.

Classical systems in phase space (top). Each system consists of one massive particle in a one-dimensional potential well (red curve, lower figure). The initially compact ensemble becomes swirled up over time.]]


In classical mechanics, an ensemble is represented by a probability density function defined over the system's phase space.[1] While an individual system evolves according to Hamilton's equations, the density function (the ensemble) evolves over time according to Liouville's equation.


In a mechanical system with a defined number of parts, the phase space has n generalized coordinates called q1, ... qn, and n associated canonical momenta called p1, ... pn. The ensemble is then represented by a joint probability density function ρ(p1, ... pn, q1, ... qn).

在一个有确定数量的物体的力学系统中,相空间有广义坐标,与其相关的正则动量叫做 p1, ... pn。系综则由一个联合概率密度函数代表 ρ(p1, ... pn, q1, ... qn)

If the number of parts in the system is allowed to vary among the systems in the ensemble (as in a grand ensemble where the number of particles is a random quantity), then it is a probability distribution over an extended phase space that includes further variables such as particle numbers N1 (first kind of particle), N2 (second kind of particle), and so on up to Ns (the last kind of particle; s is how many different kinds of particles there are). The ensemble is then represented by a joint probability density function ρ(N1, ... Ns, p1, ... pn, q1, ... qn). The number of coordinates n varies with the numbers of particles.

如果系统中的物体数量被允许在系综中不同系统之间变化(就像在一个巨系综中,粒子数量是一个随机量) ,那么它就是一个扩展相空间上的概率分布,并且它将包括更多的变量,如粒子数量N1(第一类粒子) ,N2(第二类粒子) ,等等,直到 Ns(最后一类粒子; s代表多少不同类型的粒子)。这个系综由一个联合概率密度函数表示 ρ(N1, ... Ns, p1, ... pn, q1, ... qn)。坐标的数目随粒子的数目而变化。

Any mechanical quantity X can be written as a function of the system's phase. The expectation value of any such quantity is given by an integral over the entire phase space of this quantity weighted by ρ:


[math]\displaystyle{ \langle X \rangle = \sum_{N_1 = 0}^{\infty} \ldots \sum_{N_s = 0}^{\infty} \int \ldots \int \rho X \, dp_1 \ldots dq_n. }[/math]

The condition of probability normalization applies, requiring


[math]\displaystyle{ \sum_{N_1 = 0}^{\infty} \ldots \sum_{N_s = 0}^{\infty} \int \ldots \int \rho \, dp_1 \ldots dq_n = 1. }[/math]

Phase space is a continuous space containing an infinite number of distinct physical states within any small region. In order to connect the probability density in phase space to a probability distribution over microstates, it is necessary to somehow partition the phase space into blocks that are distributed representing the different states of the system in a fair way. It turns out that the correct way to do this simply results in equal-sized blocks of canonical phase space, and so a microstate in classical mechanics is an extended region in the phase space of canonical coordinates that has a particular volume.[note 1] In particular, the probability density function in phase space, ρ, is related to the probability distribution over microstates, P by a factor


[math]\displaystyle{ \rho = \frac{1}{h^n C} P, }[/math]


  • h is an arbitrary but predetermined constant with the units of energy×time, setting the extent of the microstate and providing correct dimensions to ρ.[note 2]
  • h 是一个随机但是事先规定好的常数,它的单位是 energy×time,在微状态中规定了ρ的正确维度。
  • C is an overcounting correction factor (see below), generally dependent on the number of particles and similar concerns.
  • C是一个针对过度计数的修正量,大致上取决于粒子的数量。

Since h can be chosen arbitrarily, the notional size of a microstate is also arbitrary. Still, the value of h influences the offsets of quantities such as entropy and chemical potential, and so it is important to be consistent with the value of h when comparing different systems.



Typically, the phase space contains duplicates of the same physical state in multiple distinct locations. This is a consequence of the way that a physical state is encoded into mathematical coordinates; the simplest choice of coordinate system often allows a state to be encoded in multiple ways. An example of this is a gas of identical particles whose state is written in terms of the particles' individual positions and momenta: when two particles are exchanged, the resulting point in phase space is different, and yet it corresponds to an identical physical state of the system. It is important in statistical mechanics (a theory about physical states) to recognize that the phase space is just a mathematical construction, and to not naively overcount actual physical states when integrating over phase space. Overcounting can cause serious problems:

通常,相空间在多个不同的位置包含相同物理状态的重复项。这是物理状态编码为数学坐标的结果; 最简单的坐标系选择通常允许以多种方式对状态进行编码。举个例子,对于全同粒子的气体,其状态是由粒子各自的位置和动量表示的: 当两个粒子交换时,相空间中的结果点是不同的,但它相当于系统中的一个相同的物理状态。在统计力学中,我们非常需要认识到相空间只是一个数学结构,并且在相空间上积分时不要天真地重复计数实际的物理状态。重复计数过多会导致如下严重的问题:

  • Dependence of derived quantities (such as entropy and chemical potential) on the choice of coordinate system, since one coordinate system might show more or less overcounting than another.[note 3]
  • 派生量(例如熵和化学势)对坐标系选择的依赖性,因为一个坐标系可能比另一个坐标系显示更多或更少的过度计数。
  • Erroneous conclusions that are inconsistent with physical experience, as in the mixing paradox.[1]
  • 与物理经验不一致的错误结论,如混合悖论。[1]

It is in general difficult to find a coordinate system that uniquely encodes each physical state. As a result, it is usually necessary to use a coordinate system with multiple copies of each state, and then to recognize and remove the overcounting.


A crude way to remove the overcounting would be to manually define a subregion of phase space that includes each physical state only once and then exclude all other parts of phase space. In a gas, for example, one could include only those phases where the particles' x coordinates are sorted in ascending order. While this would solve the problem, the resulting integral over phase space would be tedious to perform due to its unusual boundary shape. (In this case, the factor C introduced above would be set to C = 1, and the integral would be restricted to the selected subregion of phase space.)

消除重复计数的一个粗略方法是人工定义一个只包含每个物理状态一次的相空间分区,然后排除相空间的所有其他部分。例如,在气体中,可以只包括粒子坐标x按升序排列的那些相。虽然这样可以解决这个问题,但是由于产生的相空间的边界形状将不同寻常,因此计算相空间上的积分将是非常繁琐的。(在这种情况下,上面介绍的C因子将被设置为1 ,并且积分将局限于选定的相空间分区。)

A simpler way to correct the overcounting is to integrate over all of phase space but to reduce the weight of each phase in order to exactly compensate the overcounting. This is accomplished by the factor C introduced above, which is a whole number that represents how many ways a physical state can be represented in phase space. Its value does not vary with the continuous canonical coordinates,[note 4] so overcounting can be corrected simply by integrating over the full range of canonical coordinates, then dividing the result by the overcounting factor. However, C does vary strongly with discrete variables such as numbers of particles, and so it must be applied before summing over particle numbers.


As mentioned above, the classic example of this overcounting is for a fluid system containing various kinds of particles, where any two particles of the same kind are indistinguishable and exchangeable. When the state is written in terms of the particles' individual positions and momenta, then the overcounting related to the exchange of identical particles is corrected by using[1]


[math]\displaystyle{ C = N_1! N_2! \ldots N_s!. }[/math]

This is known as "correct Boltzmann counting".



The formulation of statistical ensembles used in physics has now been widely adopted in other fields, in part because it has been recognized that the canonical ensemble or Gibbs measure serves to maximize the entropy of a system, subject to a set of constraints: this is the principle of maximum entropy. This principle has now been widely applied to problems in linguistics, robotics, and the like.

在物理学中使用的统计系综的公式现在已经被其他领域广泛采用,部分原因是大家已经认识到正则系综或吉布斯测量致力于在受到一系列约束时最大化一个系统的熵: 最大熵原则。这一原则现已广泛应用于语言学、机器人学等领域的问题。

In addition, statistical ensembles in physics are often built on a principle of locality: that all interactions are only between neighboring atoms or nearby molecules. Thus, for example, lattice models, such as the Ising model, model ferromagnetic materials by means of nearest-neighbor interactions between spins. The statistical formulation of the principle of locality is now seen to be a form of the Markov property in the broad sense; nearest neighbors are now Markov blankets. Thus, the general notion of a statistical ensemble with nearest-neighbor interactions leads to Markov random fields, which again find broad applicability; for example in Hopfield networks.

此外,物理学中的统计系综通常是建立在定域性原理的基础上的: 所有的相互作用只存在于相邻的原子或分子之间。因此,例如,格子模型,伊辛模型,磁铁材料模型(模拟自旋间的近邻相互作用)。定域性原理的统计公式现在被认为是广义的马尔可夫性的一种形式; 最接近的例子是马尔可夫毯。因此,具有近邻相互作用的系综一般会指马尔可夫随机场,它有广泛的适用性,例如可以被应用在霍普菲尔德(Hopfield) 网络。


In the discussion given so far, while rigorous, we have taken for granted that the notion of an ensemble is valid a priori, as is commonly done in physical context. What has not been shown is that the ensemble itself (not the consequent results) is a precisely defined object mathematically. For instance,


It is not clear where this very large set of systems exists (for example, is it a gas of particles inside a container?)


It is not clear how to physically generate an ensemble.


In this section, we attempt to partially answer this question.


Suppose we have a preparation procedure for a system in a physics lab: For example, the procedure might involve a physical apparatus and some protocols for manipulating the apparatus. As a result of this preparation procedure, some system is produced and maintained in isolation for some small period of time.


By repeating this laboratory preparation procedure we obtain a sequence of systems X1, X2, ....,Xk, which in our mathematical idealization, we assume is an infinite sequence of systems. The systems are similar in that they were all produced in the same way. This infinite sequence is an ensemble.

通过重复这个实验室准备程序,我们得到了一个数学中理想化的无限的系统序列X1, X2, ....,Xk。这些系统是相似的,因为它们都是以相同的方式生产的。这个无限序列是一个集合。In a laboratory setting, each one of these prepped systems might be used as input for one subsequent testing procedure. Again, the testing procedure involves a physical apparatus and some protocols; as a result of the testing procedure we obtain a yes or no answer.


Given a testing procedure E applied to each prepared system, we obtain a sequence of values

当每个准备系统都被测试程序 E测试后,我们得到了一系列的值

Meas (E, X1), Meas (E, X2),...., Meas (E, Xk). Each one of these values is a 0 (or no) or a 1 (yes).

Meas (E, X1), Meas (E, X2),...., Meas (E, Xk). 这些值中的每一个都是0或1(否或是)。

Assume the following time average exists:


[math]\displaystyle{ \sigma(E) = \lim_{N \rightarrow \infty} \frac{1}{N} \sum_{k=1}^N \operatorname{Meas}(E, X_k) }[/math]

For quantum mechanical systems, an important assumption made in the quantum logic approach to quantum mechanics is the identification of yes-no questions to the lattice of closed subspaces of a Hilbert space. With some additional technical assumptions one can then infer that states are given by density operators S so that:

对于量子力学系统,我们在用量子逻辑方法识别希伯尔(Hilbert) 空间的闭子空间格中的是否问题时做了一个重要的假设。我们在一些额外的技术假设之上,可以通过密度因子S推出:

[math]\displaystyle{ \sigma(E) = \operatorname{Tr}(E S). }[/math]

We see this reflects the definition of quantum states in general: A quantum state is a mapping from the observables to their expectation values.

这反映了量子态的定义: 量子态是一个从可观测量到其期望值的映射。



  1. This equal-volume partitioning is a consequence of Liouville's theorem, i. e., the principle of conservation of extension in canonical phase space for Hamiltonian mechanics. This can also be demonstrated starting with the conception of an ensemble as a multitude of systems. See Gibbs' Elementary Principles, Chapter I.
  2. (Historical note) Gibbs' original ensemble effectively set h = 1 [energy unit]×[time unit], leading to unit-dependence in the values of some thermodynamic quantities like entropy and chemical potential. Since the advent of quantum mechanics, h is often taken to be equal to Planck's constant in order to obtain a semiclassical correspondence with quantum mechanics.
  3. In some cases the overcounting error is benign. An example is the choice of coordinate system used for representing orientations of three-dimensional objects. A simple encoding is the 3-sphere (e. g., unit quaternions) which is a double cover—each physical orientation can be encoded in two ways. If this encoding is used without correcting the overcounting, then the entropy will be higher by k log 2 per rotatable object and the chemical potential lower by kT log 2. This does not actually lead to any observable error since it only causes unobservable offsets.
  4. Technically, there are some phases where the permutation of particles does not even yield a distinct specific phase: for example, two similar particles can share the exact same trajectory, internal state, etc.. However, in classical mechanics these phases only make up an infinitesimal fraction of the phase space (they have measure zero) and so they do not contribute to any volume integral in phase space.


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External links

  1. 1.00 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 1.11 1.12 1.13 1.14 1.15 1.16 Gibbs, Josiah Willard (1902). Elementary Principles in Statistical Mechanics. New York: Charles Scribner's Sons. 
  2. 2.0 2.1 Kittel, Charles; Herbert Kroemer (1980). Thermal Physics, Second Edition. San Francisco: W.H. Freeman and Company. pp. 31 ff. ISBN 0-7167-1088-9. 
  3. 3.0 3.1 Landau, L.D.; Lifshitz, E.M. (1980). Statistical Physics. Pergamon Press. pp. 9 ff. ISBN 0-08-023038-5. 

模板:Statistical mechanics topics

Category:Concepts in physics

分类: 物理概念

Category:Philosophy of thermal and statistical physics

类别: 热力学和统计物理学哲学

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