Master equation

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In

differential equations

– over time – of the probabilities that the system occupies each of the different states.

The name was proposed in 1940.[1]^[2]

When the probabilities of the elementary processes are known, one can write down a continuity equation for W, from which all other equations can be derived and which we will call therefore the "master” equation.

— Nordsieck, Lamb, and Uhlenbeck, On the theory of cosmic-ray showers I the furry model and the fluctuation problem (1940)

Introduction

A master equation is a phenomenological set of first-order

with regard to a continuous time variable t. The most familiar form of a master equation is a matrix form:

$${\displaystyle {\frac {d{\vec {P}}}{dt}}=\mathbf {A} {\vec {P}},}$$

where ${\displaystyle {\vec {P}}}$ is a column vector, and ${\displaystyle \mathbf {A} }$ is the matrix of connections. The way connections among states are made determines the dimension of the problem; it is either

• a d-dimensional system (where d is 1,2,3,...), where any state is connected with exactly its 2d nearest neighbors, or
• a network, where every pair of states may have a connection (depending on the network's properties).

When the connections are time-independent rate constants, the master equation represents a

Markovian

(any jumping time probability density function for state i is an exponential, with a rate equal to the value of the connection). When the connections depend on the actual time (i.e. matrix ${\displaystyle \mathbf {A} }$ depends on the time, ${\displaystyle \mathbf {A} \rightarrow \mathbf {A} (t)}$ ), the process is not stationary and the master equation reads

$${\displaystyle {\frac {d{\vec {P}}}{dt}}=\mathbf {A} (t){\vec {P}}.}$$

When the connections represent multi exponential

termed the generalized master equation:

$${\displaystyle {\frac {d{\vec {P}}}{dt}}=\int _{0}^{t}\mathbf {A} (t-\tau ){\vec {P}}(\tau )\,d\tau .}$$

The matrix ${\displaystyle \mathbf {A} }$ can also represent birth and death, meaning that probability is injected (birth) or taken from (death) the system, and then the process is not in equilibrium.

Detailed description of the matrix and properties of the system

Let ${\displaystyle \mathbf {A} }$ be the matrix describing the transition rates (also known as kinetic rates or reaction rates). As always, the first subscript represents the row, the second subscript the column. That is, the source is given by the second subscript, and the destination by the first subscript. This is the opposite of what one might expect, but it is technically convenient.

For each state k, the increase in occupation probability depends on the contribution from all other states to k, and is given by:

where ${\displaystyle P_{\ell }}$ is the probability for the system to be in the state ${\displaystyle \ell }$, while the matrix ${\displaystyle \mathbf {A} }$ is filled with a grid of transition-rate constants. Similarly, ${\displaystyle P_{k}}$ contributes to the occupation of all other states ${\displaystyle P_{\ell },}$

In probability theory, this identifies the evolution as a

.

The master equation can be simplified so that the terms with ℓ = k do not appear in the summation. This allows calculations even if the main diagonal of ${\displaystyle \mathbf {A} }$ is not defined or has been assigned an arbitrary value.

$${\displaystyle {\frac {dP_{k}}{dt}}=\sum _{\ell }(A_{k\ell }P_{\ell })=\sum _{\ell \neq k}(A_{k\ell }P_{\ell })+A_{kk}P_{k}=\sum _{\ell \neq k}(A_{k\ell }P_{\ell }-A_{\ell k}P_{k}).}$$

The final equality arises from the fact that

$${\displaystyle \sum _{\ell ,k}(A_{\ell k}P_{k})={\frac {d}{dt}}\sum _{\ell }(P_{\ell })=0}$$

because the summation over the probabilities ${\displaystyle P_{\ell }}$ yields one, a constant function. Since this has to hold for any probability ${\displaystyle {\vec {P}}}$ (and in particular for any probability of the form ${\displaystyle P_{\ell }=\delta _{\ell k}}$ for some k) we get

$${\displaystyle \sum _{\ell }(A_{\ell k})=0\qquad \forall k.}$$

Using this we can write the diagonal elements as

$${\displaystyle A_{kk}=-\sum _{\ell \neq k}(A_{\ell k})\Rightarrow A_{kk}P_{k}=-\sum _{\ell \neq k}(A_{\ell k}P_{k}).}$$

The master equation exhibits detailed balance if each of the terms of the summation disappears separately at equilibrium—i.e. if, for all states k and ℓ having equilibrium probabilities ${\displaystyle \pi _{k}}$ and ${\displaystyle \pi _{\ell }}$,

These symmetry relations were proved on the basis of the time reversibility of microscopic dynamics (microscopic reversibility) as Onsager reciprocal relations.

Examples of master equations

Many physical problems in classical, quantum mechanics and problems in other sciences, can be reduced to the form of a master equation, thereby performing a great simplification of the problem (see mathematical model).

The

between the states of the system (non-diagonal elements of the density matrix).

Another special case of the master equation is the

.

Stochastic chemical kinetics are yet another example of the Master equation. A chemical Master equation is used to model a set of chemical reactions when the number of molecules of one or more species is small (of the order of 100 or 1000 molecules).[4] The chemical Master equations is also solved for the very large models such as DNA damage signal, Fungal pathogen candida albicans for the first time.^[5]

Quantum master equations

A

which is a physical characteristic that is intrinsically quantum mechanical.

The

Theorem about eigenvalues of the matrix and time evolution

Because ${\displaystyle \mathbf {A} }$ fulfills

$${\displaystyle \sum _{\ell }A_{\ell k}=0\qquad \forall k}$$

and

$${\displaystyle A_{\ell k}\geq 0\qquad \forall \ell \neq k,}$$

one can show[7] that:

• There is at least one eigenvector with a vanishing eigenvalue, exactly one if the graph of ${\displaystyle \mathbf {A} }$ is strongly connected.
• All other eigenvalues ${\displaystyle \lambda }$ fulfill ${\displaystyle 0>\operatorname {Re} \lambda \geq 2\operatorname {min} _{i}A_{ii}}$.
• All eigenvectors ${\displaystyle v}$ with a non-zero eigenvalue fulfill ${\textstyle \sum _{i}v_{i}=0}$.

This has important consequences for the time evolution of a state.

See also

• Kolmogorov equations (Markov jump process)
• Continuous-time Markov process
• Quantum master equation
• Fermi's golden rule
• Detailed balance
• Boltzmann's H-theorem

References

• van Kampen, N. G. (1981). Stochastic processes in physics and chemistry. North Holland.
  ISBN 978-0-444-52965-7
  .
• Gardiner, C. W. (1985). Handbook of Stochastic Methods. Springer.
  ISBN 978-3-540-20882-2
  .
• Risken, H. (1984). The Fokker-Planck Equation. Springer.
  ISBN 978-3-540-61530-9
  .

External links

• Timothy Jones, A Quantum Optics Derivation (2006)