In physics and chemistry and related fields, master equations are used to describe the time-evolution of a system that can be modelled as being in exactly one of countable number of states at any given time, and where switching between states is treated probabilistically. The equations are usually a set of differential equations for the variation over time of the probabilities that the system occupies each different states.
A master equation is a phenomenological set of first-order differential equations describing the time evolution of (usually) the probability of a system to occupy each one of a discrete set of states with regard to a continuous time variable t. The most familiar form of a master equation is a matrix form:
where is a column vector (where element i represents state i), and 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 kinetic scheme, and the process is 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 depends on the time, ), the process is not stationary and the master equation reads
When the connections represent multi exponential jumping time probability density functions, the process is semi-Markovian, and the equation of motion is an integro-differential equation termed the generalized master equation:
The matrix can also represent birth and death, meaning that probability is injected (birth) or taken from (death) the system, where then, the process is not in equilibrium.
Detailed description of the matrix , and properties of the system
Let 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. The element is the rate constant that corresponds to the transition from state k to state ℓ. That is, the source is given by the second subscript, and the destination by the first subscript. For example, the subscripts in are interpreted as . 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:
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 the is not defined or has been assigned an arbitrary value.
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 and ,
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 Lindblad equation in quantum mechanics is a generalization of the master equation describing the time evolution of a density matrix. Though the Lindblad equation is often referred to as a master equation, it is not one in the usual sense, as it governs not only the time evolution of probabilities (diagonal elements of the density matrix), but also of variables containing information about quantum coherence between the states of the system (non-diagonal elements of the density matrix).
Another special case of the master equation is the Fokker-Planck equation which describes the time evolution of a continuous probability distribution. Complicated master equations which resist analytic treatment can be cast into this form (under various approximations), by using approximation techniques such as the system size expansion.
- Markov process
- Fermi's golden rule
- Detailed balance
- Boltzmann's H-theorem
- Continuous-time Markov process
||This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. (June 2011)|
- 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 3-540-20882-8.
- Risken, H. (1984). The Fokker-Planck Equation. Springer. ISBN 3-540-61530-X.
- Timothy Jones, A Quantum Optics Derivation (2006)