( the best time)

Physics time

Why do we care about Statistical Mechanics?

It relates the

 microscopic 

with the

MACROSCOPIC

( the best time)

Physics time

Why do we care about Statistical Mechanics?

It relates the

 microscopic 

with the

MACROSCOPIC

( the best time)

Why do we care about Statistical Mechanics?

Physics time

An ising model recap:
part 2
 

Ludmila Augusta Soares Botelho

Physics time

$$(x,y,z)$$

$$m$$

$$m$$

$$\vec{p}$$

$$(x,y,z, p_x,p_y,p_z)$$

$$(x,y,z)$$

$$\vec{v}$$

Statistical  mechanics

$$m$$

$$(x,y,z, p_x,p_y,p_z)$$

  • Microstate

Statistical  mechanics

mm

j=(x,y,z,px,py,pz)j=(x,y,z, p_x,p_y,p_z)

  • Microstate

j=(x1,...,x3np1,...,p3N)j=(x_1,..., x_{3n} p_1,...,p_{3_N})

- For NN particles:

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

Statistical  mechanics

mm

  • Microstate

j=(x1,...,x3np1,...,p3N)j=(x_1,..., x_{3n} p_1,...,p_{3_N})

- For NN particles:

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

Statistical  mechanics

mm

  • Microstate

j=(x1,...,x3np1,...,p3N)j=(x_1,..., x_{3n} p_1,...,p_{3_N})

- For NN particles:

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

mm

Statistical  mechanics

  • Microstate

j=(x1,...,x3np1,...,p3N)j=(x_1,..., x_{3n} p_1,...,p_{3_N})

- For NN particles:

thermodynamic Quantities 

  • Macrostate

- Equilibrium observable state

microstate

thermodynamic Quantities 

  • Macrostate

Condensed matter physics

The bridge

$$\Omega(E) =\text{Number of Microstates} $$ 

$$S = -k_b\sum_i p_i  \ln p_i$$

  • Entropy

$$S = k_b\ln W$$

$$W = \frac{N!}{\prod_i N_i }$$

$$S = k_b\ln \Omega$$

- Gibbs Entropy

- Postulate: All microstates are equally probable

$$S = -k_b\sum_i^\Omega p_\mu  \ln p_\mu$$

$$p(\mu)= \frac{1}{\Omega(E)}$$

$$ = -k_b\sum_i^\Omega \frac{1}{\Omega (E)} \ln \frac{1}{\Omega (E)}$$

$$= - k_b\ln \frac{1}{\Omega (E)}$$

$$S = k_b\ln {\Omega (E)}$$

The bridge

  • Entropy

$$S = k_b\ln \Omega$$

- State Function/Fundamental Equation

$$S = S(U,V,N)$$

$$ \frac{1}{T}= \frac{\partial S}{\partial U}$$

$$ \frac{p}{T}= \frac{\partial S}{\partial U}$$

$$ \frac{-\mu}{T}= \frac{\partial S}{\partial U}$$

temperature

pressure

chemical potential

The bridge

  • Helmholtz Free Energy

$$F= U - TS$$

$$U =-\vec{\mu} \vec{H}$$

$$m =-\left. \left( \frac{\partial F}{\partial H} \right)\right |_{T,N}$$

$$z$$

$$\vec{H}$$

- Legendre Transformation for internal energy \(U = Q +W \)

$$\chi (T,H) =-\left. \left( \frac{\partial F}{\partial H} \right)\right |_{T,N}$$

$$U =-\mu_0 {H}$$

$$ \rightarrow \frac{CH}{T}$$

$$M =N\langle{m} \rangle$$

$$\mathrm{d}F= M\mathrm{d}H - S\mathrm{d}T$$

Probability DISTRIBUTION

with the

MACROSCOPE

Canonical Ensemble

S

R

~

~

~

(T, V, N)

= E

total

$$E_ = E_S + E_R$$

$$S(E_R) =k_b \ln \Omega (E_R)$$

$$ \Omega(E_R) = \exp\left[\frac{S(E_R)}{k_b} \right]$$

$$ P(E_S)\propto \Omega(E - E_S)$$

$$E_R = E-E_S$$

$$ P_j= \alpha \Omega(E - E_j)$$

$$ \ln P_j= \ln \alpha + \ln \Omega(E) + \left[ \frac{\partial \ln \Omega(E_R)}{\partial E}\right]_{E_R=E} (-E_j)$$

$$+  \frac{1}{2}\left[ \frac{\partial^2 \ln \Omega(E)}{\partial E^2}\right]_{E_R=E}(-E_j)^2 +\cdots$$

$$\frac{1}{k_B}\frac{\partial S}{\partial U}=\frac{1}{k_BT}$$

$$\frac{1}{k_B}\frac{\partial^2 S}{\partial U^2}=\frac{1}{k_B}\frac{\partial}{\partial U}\left(\frac{1}{T}\right)$$

$$ \rightarrow 0$$

$$ \ln P_j= c -\frac{1}{k_b T}E_j$$

$$ P_j= \frac{\exp(-\beta E_j)}{\sum_k \exp(-\beta E_j)}$$

partition function

$$\sum_j E_j P_j = \langle E \rangle = U$$

$$\sum_j P_j = 1$$

$$ f(\{P_j\}, \lambda_1, \lambda_2)= -k_b\sum_j P_j \ln P_j - \lambda_1 \left( \sum_j P_j -1 \right)$$

$$ - \lambda_2 \left( \sum_j E_jP_j -U \right)$$

$$\frac{\partial f}{\partial P_j}= -k_b P_j \ln P_j -k_B -  \lambda_1  -\lambda_2 E$$

$$ P_j= \frac{\exp(-\beta E_j)}{\sum_k \exp(-\beta E_j)}$$

Partition Function

  • Thermodynamic Limit

$$ F = -\frac{1}{\beta} \ln Z$$

$$ Z \rightarrow \exp (-\beta F)$$

$$ Z = \sum_j \exp(-\beta E_j)$$

$$ = \sum_E  \exp[-\ln\Omega(E)\beta E]$$

$$ Z \sim \exp[- \min\{E -TS(E)\}$$

$$S(E) =k_b \ln \Omega (E)$$

quantum mechanics

$$\mathcal{H} = -J \sum_{i} s_i s_{i+1} - h\sum_i^N s_i$$

$$\mathcal{H} = -J \sum_{i} \sigma_i^z \sigma_{i+1}^z $$

Pauli Matrices

Reduces to classical version

$$\mathcal{H} = -J \sum_{i} \sigma_i^z \sigma_{i+1}^z - h\sum_i^N \sigma_i^z$$

\sigma^x= \begin{pmatrix} 0 & 1 \\ 1 & 0 \end{pmatrix}
\sigma^y= \begin{pmatrix} 0 & -i \\ i & 0 \end{pmatrix}
\sigma^z= \begin{pmatrix} 1 & 0 \\ 0 & -1 \end{pmatrix}

$$E = -J \sum_{i} \sigma_i \sigma_{i+1} $$

\{

transverse field

energy levels  \( \sigma_i =\pm 1 \)

diagonal!

\{
  • It has phase transition!

-Ferromagnetic vs Paramagnet

$$ \sigma_i^\alpha = I^{\otimes i-1} \otimes \sigma^\alpha I^{\otimes N-i}  $$

$$\mathcal{H} = -J \sum_{i} \sigma_i^z \sigma_{i+1}^z - h\sum_i^N \sigma_i^x$$

quantum mechanics

$$\mathcal{H} = -J \sum_{i} \sigma_i^z \sigma_{i+1}^z - h\sum_i^N \sigma_i^x$$

$$\mathcal{H} = - \frac{1}{2} \sum_{i}^N \left[ J_x \sigma_i^x \sigma_{i+1}^x +J_y \sigma_{i}^y \sigma_{i+1}^y+  J_z \sigma_i^z \sigma_{i+1}^z - h\sigma_i^z\right]$$

$$\mathcal{H} = - \frac{1}{2} \sum_{i}^N \left[ J_x \sigma_i^x \sigma_{i+1}^x +J_y \sigma_{i}^y \sigma_{i+1}^y+  - h\sigma_i^z\right]$$

$$J_x = \left( \frac{1+ \gamma}{2} \right)$$

$$J_y = \left( \frac{1- \gamma}{2} \right)$$

- Tensor Networks

- Quantum Many-Body Problems

  • Heisenberg Model
  • XY Model
  • 1D Quantum Ising Model

$$\mathcal{H} = -J \sum_{i} \sigma_i^z \sigma_{i+1}^z - h\sum_i^N \sigma_i^x$$

$$J > 0$$

$$J < 0$$

$$J = ?$$

$$\mathcal{H} = -J \sum_{i} \sigma_i^z \sigma_{i+1}^z$$

Ground state

Spin Glass Model

$$\mathcal{H} = - \sum_{i,j} J_{i,j}\sigma_i^z \sigma_{j}^z$$

magnet

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Stats. Mechs. Recap

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Stats. Mechs. Recap

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