LBan-II: Difference between revisions

From Disordered Systems Wiki
Jump to navigation Jump to search
 
(30 intermediate revisions by the same user not shown)
Line 31: Line 31:
'''Note''' that using our notation the 1D front is both an interface and a directed polymer
'''Note''' that using our notation the 1D front is both an interface and a directed polymer


=Thermal Interfaces=
=Directed Polymers on a lattice=


* The dynamics is overdamped, so that we can neglect the inertial term.
 
The Langevin equation of motion is
 
[[File:SketchDPRM.png|thumb|left|Sketch of the discrete Directed Polymer model. At each time the polymer grows either one step left either one step right.  A random energy <math> V(\tau,x)</math> is associated at each node and the total energy is simply <math> E[x(\tau)] =\sum_{\tau=0}^t V(\tau,x)</math>. ]]
 
 
We introduce a lattice model for the directed polymer (see figure). In a companion notebook we provide the implementation of the powerful Dijkstra algorithm. Dijkstra allows to identify the minimal  energy among the exponential number of configurations <math> x(\tau)</math>
<center> <math>
<center> <math>
\partial_t h(r,t)= - \mu \frac{\delta E_{pot}}{\delta h(r,t)} + \eta(r,t)
E_{\min} = \min_{x(\tau)} E[x(\tau)].
</math></center>
</math></center>
The first term <math> \delta E_{pot}/\delta h(r,t) </math> is the elastic force trying to smooth the interface, the mobility <math> \mu </math> is the inverse of the viscosity. The second term is the Langevin noise. It is Guassian and defined by
 
<center> <math>
We are also interested in the ground state configuration  <math> x_{\min}(\tau) </math>.
\langle \eta(r,t) \rangle =0, \; \langle \eta(r',t')\eta(r,t) \rangle = 2 d D \delta^d(r-r') \delta(t-t')
For both quantities we expect scale invariance with two exponents  <math> \theta, \zeta</math> for the energy and for the roughness
<center>
<math>
E_{\min} = c_\infty t + \kappa_1 t^{\theta}\chi,   \quad x_{\min}(t/2)) \sim  \kappa_2 t^{\zeta} \tilde \chi
</math></center>
</math></center>
The symbol <math> \langle \ldots \rangle</math> indicates the average over the thermal noise and the diffusion constant is fixed by the Einstein relation <math>
  D= \mu K_B T
</math>. We set  <math> \mu= K_B=1</math>


The potential energy of surface tension (<math>\nu </math> is the stiffness) can be expanded at the lowest order in the gradient:
<strong>Universal exponents: </strong> Both  <math> \theta, \zeta </math> are  Independent of the lattice, the disorder distribution, the elastic constants, or the boudanry conditions. 
<center> <math>  
 
E_{pot} = \nu \int d^d r\sqrt{1 +(\nabla h)^2} \sim \text{const.} + \frac{\nu}{2} \int d^d r (\nabla h)^2
<strong>Non-universal constants: </strong> <math> c_\infty,\kappa_1, \kappa_2 </math>  are of  order 1 and depend on the  lattice, the disorder distribution, the elastic constants... However  <math> c_\infty  </math> is independent on the boudanry conditions!
 
<strong>Universal distributions: </strong> <math> \chi, \tilde \chi </math> are instead universal, but depends on the boundary condtions.  Starting from 2000 a magic connection has been revealed between this model and the smallest eigenvalues of random matrices. In particular I discuss two different boundary conditions:
 
* <strong>Droplet</strong>: <math> x(\tau=0) = x(\tau=t) = 0 </math>. In this case, up to rescaling,  <math> \chi</math> is distributed as the smallest eigenvalue of a GUE random matrix (Tracy Widom distribution <math>F_2(\chi) </math>)
 
* <strong> Flat</strong>: <math> x(\tau=0) = 0 </math> while the other end <math>  x(\tau=t)  </math> is free. In this case, up to rescaling,  <math> \chi</math> is distributed as the smallest eigenvalue of a GOE random matrix (Tracy Widom distribution <math>F_1(\chi) </math>)
===Entropy and scaling relation===
 
It is useful to compute the entropy
<center>
<math>
\text{Entropy}= \ln\binom{t}{\frac{t-x}{2}} \approx t \ln 2 -\frac{x^2}{2 t} +O(x^4)
</math></center>
From which one could guess from dimensional analysis 
<center>
<math>
\theta=2 \zeta-1
</math></center>
</math></center>
Hence, we have the Edwards Wilkinson equation:
This relation is actually exact also for the continuum model.
 
=Directed polymers in the continuum=
We now reanalyze the previous problem in the presence of quenched disorder. 
Instead of discussing the case of interfaces, we will focus on directed polymers. 
Let us consider polymers <math>x(\tau)</math> of length <math>t</math>. 
The energy associated with a given polymer configuration can be written as 
<center>
<math>
E[x(\tau)] = \int_0^t d\tau \, \left[ \frac{1}{2} \left( \frac{dx}{d\tau} \right)^2 + V(x(\tau), \tau) \right]
</math>
</center>
The first term describes the elastic energy of the polymer, 
while the second one is the disordered potential, which we assume to be 
<center>
<math>
\overline{ V(x,\tau) } = 0, \qquad
\overline{ V(x,\tau) V(x',\tau') } = D \, \delta(x-x') \, \delta(\tau-\tau') .
</math>
</center>
where 'D' is the disorder strength.
 
 
== Polymer partition function and propagator of a quantum particle==
 
Let us consider polymers starting in  <math>0 </math>, ending in <math>x </math> and at thermal equilibrium at  temperature <math>T</math>. The partition function of the model writes as
<center> <math>
<center> <math>
\partial_t h(r,t)= \nu \nabla^2 h(r,t) + \eta(r,t)
Z(x,t) =\int_{x(0)=0}^{x(t)=x} {\cal D} x(\tau) \exp\left[- \frac{1}{T} \int_0^t d \tau \frac{1}2(\partial_\tau x)^2 +V(x(\tau),\tau)\right]
</math></center>
</math></center>
=== Scaling Invariance===
Here, the partition function is written as a sum over all possible paths, corresponding to all possible polymer configurations that start at <math>0</math> and end at <math>x</math>, weighted by the appropriate Boltzmann factor.
The equation enjoys of a continuous symmetry because <math> h(r,t) </math> and <math> h(r,t)+c </math> cannot be distinguished. This is a condition of scale invariance:
 
<center> <math>  
 
h(b r, b^z t) \overset{in law}{\sim}  b^{\alpha} h(r,t)
Let's perform the following change of variables: <math>\tau=i t' </math>. We also identifies <math>T</math> with <math>\hbar</math> and <math> \tilde t= -i t </math> as the time.
</math></center>
Here <math>  
z, \alpha
</math> are the dynamic and the roughness exponent respectively. From dimensional analysis
<center> <math>
<center> <math>
b^{\alpha-z} \partial_t h(r,t)= b^{\alpha-2} \nabla^2 h(r,t) +b^{-d/2-z/2} \eta(r,t)
Z(x,\tilde t) =\int_{x(0)=0}^{x(\tilde t)=x} {\cal D} x(t') \exp\left[ \frac{i}{\hbar} \int_0^{\tilde t} d t' \frac{1}2(\partial_{t'} x)^2 -V(x(t'),t')\right]
</math></center>
</math></center>
From which you get <math> z=2 </math> in any dimension and a rough interface below <math> d=2 </math> with <math> \alpha =(2-d)/2 </math>.


== Width of the interface ==
Note that <math>  S[x]= \int_0^{\tilde t} d t' \frac{1}2(\partial_{t'} x)^2 -V(x(t'),t')</math> is the classical action of a particle with kinetic energy  <math> \frac{1}2(\partial_\tau x)^2</math> and  time dependent potential <math> V(x(\tau),\tau)</math>, evolving from time zero to time <math> \tilde t</math>.
From the Feymann path integral formulation, <math> Z[x,\tilde t]</math>  is the propagator of the quantum particle.
 


Consider a 1-dimensional line of size L with periodic boundary conditions.
=== Feynman-Kac formula===
We consider the width square of the interface
Let's derive the Feyman Kac formula for  <math>Z(x,t)</math> in the general case:
<center> <math>  
* First, focus on free paths and introduce the following probability
w_2(t) = \left[\int_0^L \frac{d r}{L} \left(h(r,t) - \int_0^L \frac{dr}{L} h(r,t)\right)\right]^2
<center> <math>
P[A,x,t] =\int_{x(0)=0}^{x(t)=x} {\cal D} x(\tau) \exp\left[- \frac{1}{T} \int_0^t d \tau \frac{1}2(\partial_\tau x)^2 \right] \delta\left(   \int_0^t d \tau V(x(\tau),\tau)-A \right)
</math></center>
</math></center>
It is useful to introduce the Fourier modes:
* Second, the moments generating function
<center> <math>  
<center> <math>
\hat h_q(t)= \frac{1}{L} \int_0^L e^{iqr} h(r,t), \quad h(r,t)= \sum_q e^{-iqr} \hat h_q(t)
Z_p(x ,t) = \int_{-\infty}^\infty d A e^{-p A} P[A,x,t] =\int_{x(0)=0}^{x(t)=x} {\cal D} x(\tau) e^{-\frac{1}{T} \int_0^t d \tau \frac{1}2(\partial_\tau x)^2  -p \int_0^t d \tau  V(x(\tau),\tau)}
</math></center>
</math></center>
Here <math> q=2 \pi n/L, n=\ldots ,-1,0,1,\ldots</math> and recall <math> \int_0^L d r e^{iqr}= L \delta_{q,0} </math>.
* Third, consider free paths evolving up to <math>t+dt</math> and reaching <math>x</math> :
=== Solution in the Fourier space===
<center> <math>
show that the  EW equation writes
Z_p(x,t+dt)= \left\langle e^{-p \int_0^{t+dt} d \tau  V(x(\tau),\tau)}\right\rangle=  \left\langle e^{-p \int_0^{t} d \tau  V(x(\tau),\tau)}\right\rangle e^{-p V(x,t) d t } =[1 -p V(x,t) d t +\dots]\left\langle Z_p(x-\Delta x,t) \right\rangle_{\Delta x}
<center> <math>  
\partial_t \hat h_q(t)= -\nu q^2 \hat h_q(t) + \eta_q(t), \quad \text{with} \; \langle \eta_{q_1}(t')   \eta_{q_2}(t)\rangle =\frac{2 T}{L} \delta_{q_1,-q_2}\delta(t-t')  
</math></center>
</math></center>
Here  <math>  \langle \ldots \rangle</math> is the average over all free paths, while  <math>  \langle \ldots \rangle_{\Delta x}</math> is the average over the last jump, namely  <math>  \langle \Delta x \rangle=0
</math> and  <math>  \langle \Delta x^2 \rangle=T d t  </math>.
* At the lowest order we have
<center> <math>
Z_p(x,t+dt)= Z_p(x,t) +dt \left[ \frac{T}{2} \partial_x^2 Z_p -p V(x,t) Z_p \right] +O(dt^2)
</math></center>
Replacing <math> p=1/T</math> we obtain the partition function is the solution of the Schrodinger-like equation:
<center> <math>
\partial_t Z(x,t) =-  \hat H Z = - \left[ -\frac{T}{2}\frac{d^2 }{d x^2} + \frac{V(x,\tau)}{T}\right] Z(x,t)
</math>
<math>
Z[x,t=0]=\delta(x)
</math>
</center>
===Remarks===
<Strong>Remark 1:</Strong>


The solution of this first order linear equation writes
This equation is a diffusive equation with multiplicative noise <math>V(x,\tau)/T</math> . Edwards Wilkinson is instead a diffusive equation with additive noise.  
<center> <math>  
\hat h_q(t)= \hat h_q(0) e^{-\nu q^2  t} +\int_0^t d s e^{- \nu q^2 (t-s)} \eta_q(s)
</math></center>
=== Flat initial condition ===
Assume that the interface is initially flat, namely <math> \hat h_q(0) =0 </math>. Show that
<center> <math>
\langle \hat h_q(t) \hat h_{-q}(t) \rangle  =\begin{cases}
\dfrac{T(1 - e^{-2\nu q^{2}t})}{L \nu q^{2}}, & q \neq 0, \\[1.2em]
\frac{2 T}{L} t, & q = 0.
\end{cases}
</math></center>


<Strong>Remark 2:</Strong>
This hamiltonian is time dependent because of the multiplicative noise <math>V(x,\tau)/T</math>. For a <Strong> time independent </Strong> hamiltonian, we can use the spectrum of the operator. In general we will have to parts:
* A discrete set of eigenvalues <math>E_n</math> with the eigenstates <math>\psi_n(x)</math>
* A continuum part where the states <math>\psi_E(x)</math> have energy <math>E</math>. We define the density of states  <math>\rho(E)</math>, such that the number of states with energy in (<math>E, E + dE</math>) is <math>\rho(E) dE </math>.


* the width square is a random variable and we can compute its mean:
In this case <math> Z[x,t] </math> can be written has the sum of two contributions:
<center> <math>  
<center> <math>
w_2(t) = \sum_{q\ne 0} |\hat h_q(t)|^2  =\sum_{q\ne 0} (\hat h_q(t) \hat h_{-q}(t)) ^2
Z[x,t] = \left( e^{- \hat H t} \right)_{0 \to x}= \sum_n \psi_n(0) \psi_n^*(x) e^{- E_n t} + \int_0^\infty dE \, \rho(E) \psi_E(0) \psi_E^*(x) e^{- E t}.
</math>
</center>
In absence of disorder, one can find the propagator of the free particle, that, in the original variables, writes:
<center> <math>
Z_{\text{free}}(x,t)=\frac{e^{-x^2/(2Tt)}}{\sqrt{2 \pi Tt}}
</math></center>
</math></center>
and the displacement of a point of the interface  that we tagg
  <math> \langle h(r,t)^2\rangle = \sum_q \langle \hat h_q(t) \hat h_{-q}(t) \rangle  </math>. Comment about the roughness and the short times growth.

Latest revision as of 07:56, 16 September 2025

Introduction: Interfaces and Directed Polymers

The physical properties of many materials are governed by manifolds embedded in them. Examples include: dislocations in crystals, domain walls in ferromagnets or vortex lines in superconductors. We fix the following notation: - : spatial dimension of the embedding medium – : internal dimension of the manifold – : dimension of the displacement (or height) field

These satisfy the relation:

We focus on two important cases:

Directed Polymers (d = 1)

The configuration is described by a vector function: , where is the internal coordinate. The polymer lives in dimensions.

Examples: vortex lines, DNA strands, fronts.

Although polymers may form loops, we restrict to directed polymers, i.e., configurations without overhangs or backward turns.

Interfaces (N = 1)

The interface is described by a scalar height field: , where is the internal coordinate and represents time.

Examples: domain walls and propagating fronts

Again we neglect overhangs or pinch-off: is single-valued

Note that using our notation the 1D front is both an interface and a directed polymer

Directed Polymers on a lattice

Sketch of the discrete Directed Polymer model. At each time the polymer grows either one step left either one step right. A random energy is associated at each node and the total energy is simply .


We introduce a lattice model for the directed polymer (see figure). In a companion notebook we provide the implementation of the powerful Dijkstra algorithm. Dijkstra allows to identify the minimal energy among the exponential number of configurations

We are also interested in the ground state configuration . For both quantities we expect scale invariance with two exponents for the energy and for the roughness

Universal exponents: Both are Independent of the lattice, the disorder distribution, the elastic constants, or the boudanry conditions.

Non-universal constants: are of order 1 and depend on the lattice, the disorder distribution, the elastic constants... However is independent on the boudanry conditions!

Universal distributions: are instead universal, but depends on the boundary condtions. Starting from 2000 a magic connection has been revealed between this model and the smallest eigenvalues of random matrices. In particular I discuss two different boundary conditions:

  • Droplet: . In this case, up to rescaling, is distributed as the smallest eigenvalue of a GUE random matrix (Tracy Widom distribution )
  • Flat: while the other end is free. In this case, up to rescaling, is distributed as the smallest eigenvalue of a GOE random matrix (Tracy Widom distribution )

Entropy and scaling relation

It is useful to compute the entropy

From which one could guess from dimensional analysis

This relation is actually exact also for the continuum model.

Directed polymers in the continuum

We now reanalyze the previous problem in the presence of quenched disorder. Instead of discussing the case of interfaces, we will focus on directed polymers. Let us consider polymers of length . The energy associated with a given polymer configuration can be written as

The first term describes the elastic energy of the polymer, while the second one is the disordered potential, which we assume to be

where 'D' is the disorder strength.


Polymer partition function and propagator of a quantum particle

Let us consider polymers starting in , ending in and at thermal equilibrium at temperature . The partition function of the model writes as

Here, the partition function is written as a sum over all possible paths, corresponding to all possible polymer configurations that start at and end at , weighted by the appropriate Boltzmann factor.


Let's perform the following change of variables: . We also identifies with and as the time.

Note that is the classical action of a particle with kinetic energy and time dependent potential , evolving from time zero to time . From the Feymann path integral formulation, is the propagator of the quantum particle.


Feynman-Kac formula

Let's derive the Feyman Kac formula for in the general case:

  • First, focus on free paths and introduce the following probability
  • Second, the moments generating function
  • Third, consider free paths evolving up to and reaching  :

Here is the average over all free paths, while is the average over the last jump, namely and .

  • At the lowest order we have

Replacing we obtain the partition function is the solution of the Schrodinger-like equation:

Remarks

Remark 1:

This equation is a diffusive equation with multiplicative noise . Edwards Wilkinson is instead a diffusive equation with additive noise.

Remark 2: This hamiltonian is time dependent because of the multiplicative noise . For a time independent hamiltonian, we can use the spectrum of the operator. In general we will have to parts:

  • A discrete set of eigenvalues with the eigenstates
  • A continuum part where the states have energy . We define the density of states , such that the number of states with energy in () is .

In this case can be written has the sum of two contributions:

In absence of disorder, one can find the propagator of the free particle, that, in the original variables, writes: