Ph · Physics

How Is the Fermi–Dirac Distribution Derived?

Published By the 9to5Science editorial team

At a glance

  1. 1. Count allowed arrangements

    For each energy level, count ways to place fermions in its available states, allowing at most one fermion per state.

  2. 2. Apply constraints

    Maximize the total number of arrangements while fixing the system’s average particle number and energy.

  3. 3. Solve for occupancy

    The maximizing average occupation per state is one divided by one plus an exponential involving energy, chemical potential, and temperature.

  4. 4. Check the result

    At energy equal to the chemical potential, the occupation is one-half; far above it, occupation is small.

The Fermi–Dirac distribution gives the average occupation of a one-particle state for a system of fermions in thermal equilibrium. It applies to particles such as electrons, which obey the Pauli exclusion principle: each available one-particle state can contain either zero or one particle.

The derivation below counts the number of possible arrangements at each energy, then finds the most probable distribution while holding the average energy and particle number fixed. This is equivalent to the equilibrium result from the grand canonical ensemble.

Count the fermion arrangements

Suppose energy level ii has energy ϵi\epsilon_i and contains gig_i distinct one-particle states. If NiN_i fermions occupy those states, the number of ways to choose which states are occupied is

Wi=(giNi).W_i=\binom{g_i}{N_i}.

The total number of arrangements across all energy levels is the product

W=∏i(giNi).W=\prod_i \binom{g_i}{N_i}.

This counting is where the exclusion principle enters. Because a state can be occupied at most once, the choices are combinations of NiN_i states from gig_i, not unrestricted assignments of particles to states.

For large numbers of states and particles, use Stirling’s approximation, ln⁡n!≈nln⁡n−n\ln n!\approx n\ln n-n. Taking the logarithm of the multiplicity gives

ln⁡W≈∑i[giln⁡gi−Niln⁡Ni−(gi−Ni)ln⁡(gi−Ni)].\ln W \approx \sum_i \left[g_i\ln g_i-N_i\ln N_i-(g_i-N_i)\ln(g_i-N_i)\right].

Maximize subject to fixed energy and particle number

The distribution must satisfy the constraints

∑iNi=N,∑iϵiNi=E,\sum_i N_i=N,\qquad \sum_i \epsilon_iN_i=E,

where NN and EE are the total particle number and energy. Maximize ln⁡W\ln W subject to these constraints using Lagrange multipliers α\alpha and β\beta. For each level, differentiation with respect to NiN_i gives

∂∂Ni(ln⁡W−α∑jNj−β∑jϵjNj)=ln⁡(gi−NiNi)−α−βϵi=0.\frac{\partial}{\partial N_i}\left(\ln W-\alpha\sum_jN_j-\beta\sum_j\epsilon_jN_j\right) =\ln\left(\frac{g_i-N_i}{N_i}\right)-\alpha-\beta\epsilon_i=0.

Rearranging,

Nigi=1exp⁡(α+βϵi)+1.\frac{N_i}{g_i}=\frac{1}{\exp(\alpha+\beta\epsilon_i)+1}.

The ratio Ni/giN_i/g_i is the average occupation per one-particle state at energy ϵi\epsilon_i. In thermal equilibrium, β=1/(kT)\beta=1/(kT) and α=−μ/(kT)\alpha=-\mu/(kT), where TT is absolute temperature, kk is Boltzmann’s constant, and μ\mu is the chemical potential. Substitution yields the Fermi–Dirac distribution:

f(ϵi)=Nigi=1exp⁡((ϵi−μ)/(kT))+1.f(\epsilon_i)=\frac{N_i}{g_i}=\frac{1}{\exp\left((\epsilon_i-\mu)/(kT)\right)+1}.

The chemical potential adjusts to give the required average number of particles. For a system with a fixed particle number, it is generally determined from the condition ∑igif(ϵi)=N\sum_i g_i f(\epsilon_i)=N.

Worked example

Consider a one-particle state with energy ϵ=μ+kTln⁡3\epsilon=\mu+kT\ln 3. Its energy is above the chemical potential, so

f(ϵ)=1exp⁡(ln⁡3)+1=13+1=14.f(\epsilon)=\frac{1}{\exp(\ln 3)+1}=\frac{1}{3+1}=\frac14.

Thus, in equilibrium, that state has a 25% average occupation probability. At ϵ=μ\epsilon=\mu, the exponent is zero and f=1/2f=1/2. A state well below μ\mu is more likely to be occupied, while one well above μ\mu is less likely to be occupied.

Why the same formula appears in the grand canonical ensemble

In that ensemble, a single fermion state of energy ϵ\epsilon has two possible occupations: n=0n=0 or n=1n=1. Its grand partition function is

Z=1+exp⁡[−(ϵ−μ)/(kT)].Z=1+\exp[-(\epsilon-\mu)/(kT)].

The probability that the state is occupied is its occupied-state statistical weight divided by ZZ:

P(n=1)=exp⁡[−(ϵ−μ)/(kT)]1+exp⁡[−(ϵ−μ)/(kT)]=1exp⁡[(ϵ−μ)/(kT)]+1.P(n=1)=\frac{\exp[-(\epsilon-\mu)/(kT)]}{1+\exp[-(\epsilon-\mu)/(kT)]} =\frac{1}{\exp[(\epsilon-\mu)/(kT)]+1}.

Since nn can only be zero or one, its expected occupation equals P(n=1)P(n=1). This gives the same formula directly. The counting derivation describes the most probable distribution for a large system; the grand canonical derivation gives the mean occupation of an individual state.

Common mistakes and limits

  • Confusing a level with a state: If an energy level is degenerate, gig_i counts its distinct states. The formula gives occupation per state; the expected number of particles in the whole level is gif(ϵi)g_i f(\epsilon_i).
  • Dropping the plus one: The denominator’s +1+1 distinguishes fermions from the Bose–Einstein distribution, whose denominator has a −1-1.
  • Using the wrong sign: The exponent is (ϵ−μ)/(kT)(\epsilon-\mu)/(kT). Occupation must decrease as energy rises above the chemical potential.
  • Treating it as a universal interacting-particle formula: The basic derivation assumes independent states, or a setting where particles can be treated as independent quasiparticles. Strong interactions can require a more detailed model.

As temperature approaches zero, the distribution approaches a sharp step: states below the chemical potential are occupied and those above it are empty. At nonzero temperature, the transition is smooth.

References

  1. Statistical Mechanics, Third Edition — R. K. Pathria and Paul D. Beale
  2. Thermal Physics — Charles Kittel and Herbert Kroemer

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