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1
General Concept of Polaron and its Manifestations in Transport and
Spectral Propertiesn
A. S. MishchenkoRIKEN (Institute of Physical and Chemical Research), Japan
Electron-phonon interaction. Origin and Hamiltonian.Properties of ground state.Dynamical properties. What to look at? Why dificult? How to overcome?Frohlich 3D polaron: spectral function and optical conductivityPolaron mobility: 5 different regimes.Hole in t-J-Holstein model.
2
A. S. MishchenkoRIKEN (Institute of Physical and Chemical Research), Japan
General Concept of Polaron and its Manifestations in Transport and
Spectral Propertiesn
3
A. S. MishchenkoRIKEN (Institute of Physical and Chemical Research), Japan
ToTokyo500 m
FromTokyo
General Concept of Polaron and its Manifestations in Transport and
Spectral Propertiesn
• Electron-phonon
interaction.
• Origin and Hamiltonian.
4
Polaron: momentum space
Where this interaction comes from?
6
H0= E0 Σi Ci+ Ci
+ t Σij Ci+ Cj
Band structure in rigid lattice.
E0
E0-αu
Hint= α Σi Ci+ Ci u u ~ (bi
+ + bi)
Hint= γ Σi Ci+ Ci (bi
+ + bi)
Brething
7
H0= E0 Σi Ci+ Ci
+ t Σij Ci+ Cj
Band structure in rigid lattice.
E0
E0-αu
Hint= α Σi Ci+ Ci u u ~ (bi
+ + bi)
Hint= γ Σi Ci+ Ci (bi
+ + bi)
Holstein
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H0= E0 Σi Ci+ Ci
+ t Σij Ci+ Cj
Band structure in rigid lattice.
t-αu
Hint= α Σij Ci+ Cj u u ~ (bi
+ + bi)
Hint= γ Σi Ci+ Cj (bi
+ + bi) t
SSH
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Polaron: momentum space
Scattered in momentum space
k-qk
-q
Polaron: momentum space
Which physical characteristics may change?
Properties of
groundstate
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Which physical characteristics may change?
1. Energy of particle2. Dispersion of particle3. Effective mass of particle4. Z-factor of particle
Which physical characteristics are interesting to study?
1. Energy of particle2. Dispersion of particle3. Effective mass of particle4. Z-factor of particle5. Structure of phonon cloud6. ……
Frohlich polaron
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GS energy Effective mass
Frohlich polaron
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Polaron cloudZ-factor
Holstein polaron
16Number of phonons in the polaron cloud
V(q)=const
• Seems that weak and strong coupling regime are separated by crossover, not by real phase transition.
• This is true for V(q)
• However, this is not true for V(k,q). SSH model.
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SSH polaron(Su-Schrieffer-Heeger)
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SSH polaron(Su-Schrieffer-Heeger)
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SSH polaron(Su-Schrieffer-Heeger)
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SSH polaron(Su-Schrieffer-Heeger)
phase diagram
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Physical properties under interest
Polaron: green function
No simple connection to measurable properties!
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Physical properties under interest: Lehman functionLehmann spectral function (LSF)
LSF has poles (sharp peaks)at the energies of stable(metastable) states. It is a measurable (in ARPES)quantity.
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Physical properties under interest: Lehmann function.Lehmann spectral function (LSF)
LSF has poles (sharp peaks)at the energies of stable(metastable) states. It is a measurable (in ARPES)quantity.
LSF can be determined from equation:
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Physical properties under interest: Z-factor and energy
Lehmann spectral function (LSF)
If the state with the lowest energy in the sector of given momentumis stable
The asymptotic behavior is
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Physical properties under interest: Z-factor and energy
The asymptotic behavior is
Dynamicalproperties.
What to look at?Why difficult?
How to overcome?
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Physical properties under interest: Lehmann function.Lehmann spectral function (LSF)
LSF can be determined from equation:
Solving of this equation is a notoriously difficult problem
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Physical properties under interest: absorption by polaron.
Such relation between imaginary-time function and spectralproperties is rather general:
Current–current corelation functionis related to optical absorption by polarons by the same expression:
ARPES
Optical absorption
μ = σ (ω→0)/en Mobility
There are a lot ofproblems where one has to solveFredholmintegral equationof the first kind
Many-particle Fermi/Boson system in imaginary times representation
Many-particle Fermi/Boson system in Matsubara representation
Optical conductivity at finite T in imaginary times representation
Image deblurring with e.g. known 2D noise K(m,ω)
K(m,ω) is a 2D x 2D noise distributon function
m and ω are2D vectors
Tomography image reconstruction (CT scan)
K(m,ω) is a 2D x 2Ddistribution function
m and ω are2D vectors
Aircraftstability
Nuclearreactor
operation
Image deblurring
A lot ofother…
What is dramaticin the problem?
Aircraftstability
Nuclearreactor
operation
Image deblurring
A lot ofother…
What is dramaticin the problem?
Ill-posed!
Ill-posed!
We cannot obtain an exact solution not becauseof some approximations of our approaches.
Instead, we have to admit that the exact solution does not exist at all!
Ill-posed!
1. No unique solution in mathematical sense No function A to satisfy the equation
2. Some additional information is required which specifies which kind of solution is expected. In orderto chose among many approximate solutions.
Ill-posed!
Physics department:Max Ent.
Engineering department:Tikhonov Regularization
Statistical department:ridge regression
Next player: stochastic methods
Ill-posed!
Because of noise present in the input data G(m)there is no unique A(ωn)=A(n) which exactly satisfies the equation. Hence, one can search for the least-square fittedsolution A(n) which minimizes:
Ill-posed!
Explicit expression:
Saw tooth noise instabilitydue to small singular values.
Ill-posed!
General formulation of methods to solve ill-posed problems in terms ofBayesian statistical inference.
Bayes theorem:
P[A|G] P[G] = P[G|A] P[A]
P[A|G] – conditional probability that the spectral function is A provided the correlation function is G
Bayes theorem:
P[A|G] P[G] = P[G|A] P[A]
P[A|G] – conditional probability that the spectral function is A provided the correlation function is G
To find it is just the analytic continuation
P[A|G] ~ P[G|A] P[A]
P[G|A] is easier problem of finding G given A: likelihood function
P[A] is prior knowledge about A:
Analytic continuation
P[A|G] ~ P[G|A] P[A]
P[G|A] is easier problem of finding G given A: likelihood function
P[A] is prior knowledge about A:
All methods to solve the above problemcan be formulated in terms of this relation
Historically first method to solve the problem of Fredholm kind I integral equation.
Tikhonov regularization method (1943)
Tikhonov regularization method (1943)
If Г is unit matrix:
Ill-posed!
Tikhonov regularizationto fight with the
saw tooth noise instability.
P[A|G] ~ P[G|A] P[A]Maximum entropy method
Likelihood(objective)function
Prior knowledge function
P[A|G] ~ P[G|A] P[A]Maximum entropy method
Prior knowledge function
D(ω) is default model
P[A|G] ~ P[G|A] P[A]Maximum entropy method
Prior knowledge function
1. One has escaped extra smoothening.
2. But one has got default model as an extra price.
P[A|G] ~ P[G|A] P[A]
Prior knowledge function
1. We want to avoid extra smoothening.
2. We want to avoid default model as an extra price.
Maximum entropy method
P[A|G] ~ P[G|A] P[A]
Stochastic methods
Both items (extra smoothening and arbitrary default model) can be somehow circumvented by the group of stochastic methods.
P[A|G] ~ P[G|A] P[A]
Stochastic methods
The main idea of the stochastic methods is:
1.Restrict the prior knowledge to the minimal possible level (positive, normalized, etc…).
2. Change the likelihood function to the likelihood functional.
P[A|G] ~ P[G|A] P[A]
Stochastic methods
The main idea of the stochastic methods is:
1.Restrict the prior knowledge to the minimal possible level (positive, normalized, etc…). Avoids default model. 2. Change the likelihood function to the likelihood functional. Avoids saw-tooth noise.
P[A|G] ~ P[G|A] P[A]
Stochastic methods
Change the likelihood function to the likelihood functional. Avoids sawtooth noise.
Stochastic methodsLikelihood functional. Avoids sawtooth noise.
Sandvik, Phys. Rev. B 1998, isthe first practical attempt to think stochastically.
Stochastic methodsLikelihood functional. Avoids sawtooth noise.
SOM was suggested in 2000.Mishchenko et al, Appendix B in Phys. Rev. B.
Stochastic methodsLikelihood functional. Avoids sawtooth noise.
What is the special need for the stochastic sampling methods?
Stochastic optimization method.
1. One has to sample through solutions A(ω) which fit the correlation function G well.
2. One has to make some weighted sum of these well solutions A(ω).
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Stochastic Optimization method.
Particular solution L(i)(ω) for LSF is presented as a sum of a number K of rectangles with some width, height and center. Initial configuration of rectangles is created by random number generator (i.e. number K and all parameters of of rectangles are randomly generated). Each particular solution L(i)(ω) is obtained by a naïve method without regularization (though, varying number K).
Final solution is obtained after M steps of such procedure L(ω) = M-1 ∑i
L(i)(ω)
Each particular solution has saw tooth noise Final averaged solution L(ω) has no saw tooth noise though not regularized with sharp peaks/edges!!!!
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Self-averaging of the saw-tooth noise.
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Self-averaging of the saw-tooth noise.
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Self-averaging of the saw-tooth noise.
Frohlich 3D polaron:spectral function
andoptical conductivity
67
Frohlich polaron: spectral function and optical conductivity.
68
Frohlich polaron: spectral function and optical conductivity.
69
Frohlich polaron: spectral function and optical conductivity.
70
Polaron mobility:
5 different regimes
71
Mobility of 1D Holstein polaron
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Band conductionregime
Hopping activationregime