1. Some background on nonequilibrium and disordered systems S. N
Some background on nonequilibrium and disordered systems S.N. Coppersmith
Equilibrium versus nonequilibrium systems — why is nonequilibrium so much harder?
Concepts from non-random systems that have proven useful for understanding some nonequilibrium systems
phase transitions
scaling and universality
Remarks on glasses
Remarks on granular materials
Remarks on usefulness of these concepts for problems in computational complexity

2. Thermal equilibrium versus the real world
Thermal equilibrium is the state matter reaches when you wait long enough without disturbing it
If energy functional E({configuration}) known, Probability(configuration)  exp(-E/kBT)
Many systems are not in thermal equilibrium
Disordered systems (equilibration times very long)
Strongly driven systems
Configuration observed typically depends on system preparation
What concepts are useful for understanding systems out of thermal equilibrium?

3. Powerful concepts that apply to equilibrium systems
Phases of matter
Liquid, solid, gas
Ferromagnet, paramagnet …..
Scale invariance near some phase transitions
Power laws
Scaling relations between exponents
Renormalization group (Kadanoff, Wilson, Fisher)
Universality

4. Concepts useful for equilibrium phase transitions have been show to apply to some other nonequilibrium situations
Phase transitions:
Depinning of driven elastic media with randomness (D. Fisher)
“Flocking” (Toner, Tu)
Oscillator synchronization (Kuramoto)
Scale invariance:
Transition to chaos (Feigenbaum)
Describes nonlinear dynamics of driven damped oscillators
Scale invariance associated with phase transition
Diffusion-limited aggregation (Witten-Sander)
“Self-organized criticality” (Bak, Tang, Wiesenfeld)

5. Concepts useful for equilibrium phase transitions have been show to apply to some other nonequilibrium situations
Phase transitions:
Depinning of driven elastic media with randomness (D. Fisher)
“Flocking” (Toner, Tu)
Oscillator synchronization (Kuramoto)
Scale invariance:
Transition to chaos (Feigenbaum)
Describes nonlinear dynamics of driven damped oscillators
Scale invariance associated with phase transition
Diffusion-limited aggregation (Witten-Sander)
“Self-organized criticality” (Bak, Tang, Wiesenfeld)

6. Scale invariance and renormalization group: the existence of scale invariance is enough to find the exponents characterizing it
Simplest example (Feigenbaum)
Consider “logistic equation” xj+1=xj(1-xj) with =3.57
xj-1/2
xj-1/2 j j
every j plotted
every other j plotted ordinate upside down
Resulting time series x1, x2, … has property that it looks the same except for a rescaling when every other point is plotted:
-z2j = zj (zj=xj-1/2)

7. Scale invariance quantitative prediction of exponent values
-z2j = zj zj zj j j

8. Scale invariance quantitative prediction of exponent values
-z2j = zj
-z2(j+1) = zj+1
Write zj+1=g(zj)
-g(g(z2j)) = g(zj)
-g(g(-zj/)) = g(zj)
This nonlinear eigenvalue equation for g only has a solution (for g’s that can be expanded in Taylor series) when =2.5029….
 Scale invariance only can occur with particular values of the scaling exponents.

9. Now show that scale invariance  exponent determined
-g(g(-y/)) = g(y)
Expand g in Taylor Series: g(y) = a - by2 + …

10. Now show that scale invariance  exponent determined
-g(g(-y/)) = g(y)
Expand g in Taylor Series: g(y) = a - by2 + …
Calculate to order y2:
-a - b(a-b(y/)2)2 = a - by2
-a - b(a2-2ab(y/)2) = a - by2

11. Now show that scale invariance  exponent determined
-g(g(-y/)) = g(y)
Expand g in Taylor Series: g(y) = a - by2 + …
Calculate to order y2:
-a - b(a-b(y/)2)2 = a - by2
-a - b(a2-2ab(y/)2) = a - by2
Equate coefficients of y0 and y2:
-a-ba2 = a; 2ab2/ = -b
-(1+ab) = 1  ab=-(1+1/)
2ab = -  -2(1+1/)= -  2-2-2=0
only ab enters
so, to this order: ≈(2+√5)/2≈2.12

12. Scale invariance is often associated with phase transitions
Examples:
Logistic map: scale invariance at value of  at which the “transition to chaos” between periodic and chaotic time series occurs
Ferromagnet: scale invariance at temperature at which there is a transition between ferromagnetic and paramagnetic phases
Percolation: scale invariance when probability of site occupation is at the value at which a giant cluster of occupied sites first appears.

13. Can other nonequilibrium systems be understood using this paradigm?
Classic nonequilibrium system: glass
Technologically useful since antiquity
Glass state is what many liquids reach when cooled quickly enough
Is glass a phase, or is it a frozen liquid?

14. Very fast rise in viscosity as temperature lowered toward glass transition
Is glass transition a phase transition, or just a kinetic freezing process?
Debenedetti & Stillinger, Nature (2001)
note: 1 year = 3107 seconds

15. Kauzmann paradox (Kauzmann, 1948)
“Entropy crisis” — extrapolation of entropies of crystal and glass would yield unphysical “negative entropy difference,” so something must happen
Crossover or phase transition?

16. Kauzmann Entropy “paradox” appears to occur at nearly the same temperature as the apparent divergence of the viscosity
Lubchenko and Wolynes (2006)

17. Glassy systems have rugged energy landscapes
Cartoon of free energy surface
Do energy barriers diverge as temperature is lowered towards glass transition?
Or, is the apparent transition just a smooth increase in barrier height plus an exponential dependence of relaxation rate on temperature?

18. Whether or not a glass transition exists is controversial
Yes:
Coincidence of Kauzmann temperature and extrapolated temperature where viscosity diverges
Nagel scaling (Dixon et al., Menon et al.)
Superexponential growth of relaxation times limits range of experimental data
No:
2-d systems have lots of configurations that interpolate smoothly between “glassy” and “crystalline” (Santen & Krauth, Donev, Stillinger, Torquato)
“The deepest and most interesting unsolved problem in solid state theory is probably the nature of glass and the glass transition. This could be the next breakthrough in the coming decade.” P.W. Anderson, Science 267, 1615 (1995)

19. What is crucial physics underlying the behavior of glasses
What is crucial physics underlying the behavior of glasses? or What other, simpler models can give insight into structural glasses?
Finite temperature important  models of spins with random couplings, at finite temperature “spin glass” (Edwards & Anderson)
Key physics is not thermal but geometric  “jamming” Consider models at zero temperature with geometrical constraints (Liu & Nagel, Biroli et al.)

20. Trying to simplify the glass problem — Spin glasses (Edwards & Anderson, 1975)
In structural glasses, disorder is not intrinsic (crystal typically has lower energy). Assume some “slow” degrees of freedom cause others to “see” random environment.
So consider model with quenched disorder and random couplings:
Spin glass models describe real physical systems (e.g., CuMn, LiYxHo1-xF4)

21. Edwards-Anderson spin glass
Ising spins with couplings of random sign
ferromagnetic bond
antiferromagnetic bond
(Ising spins at each vertex)
Three-dimensional spin glasses undergo a phase transition. Exact nature is still controversial.

22. Studies of spin glasses yield interesting results, possibly relevant to structural glasses
Infinite range spin glass model (“mean field”) -- novel broken-symmetry phase “replica symmetry-breaking”
Multi-spin couplings yield phenomenology similar to structural glasses (Kirkpatrick et al., Mezard and Parisi)
Dynamical phase transition at temperature above thermodynamic phase transition
Relevance of mean-field results to models with short-range interactions is controversial (Fisher & Huse, Bray and Moore, Newman and Stein)

23. Another point of view: glass transition is just one manifestation of “jamming”.
Liu and Nagel propose that glass transition (reached by lowering temperature) is fundamentally similar to “jamming” transition of large particles at zero temperature as density is increased.
Proposed jamming phase diagram
Temperature
Stress
Density
A.J. Liu and S.R. Nagel
In this view, glasses are fundamentally similar to granular materials.

24. Intro to granular materials
Definition: Collection of classical particles interacting only via contact forces [negligible particle deformations]
Why study granular materials?
Practical importance
industrial (e.g. construction, roads, etc.)
agricultural (e.g. grain silos)
Fundamental questions
system has many degrees of freedom, is far from thermal equilibrium
 “complex system”
+ amenable to controlled experiments
material has both solid- and liquid-like aspects

25. Interesting aspects of granular materials
Importance of dilatancy in determining response to external stresses
Nonlinear dynamics and pattern formation
Can “effective temperature” be used to describe effects of driving + collisions?
Is a given configuration a random sample from an ensemble of configurations? (Edwards)
Statistics of stress propagation in stationary systems
Jamming — as density is increased, how does the material begin to support stress?

26. Unjammed versus jammed configurations

27. Lattice models of jamming
K-core or bootstrap percolation (Schwarz, Liu, Chayes)
1) Occupy sites on a lattice with probability p,
2) If an occupied site has fewer than K occupied neighbors, empty it.

28. Lattice models of jamming
K-core or bootstrap percolation (Schwarz, Liu, Chayes; Toninelli et al.)
1) Occupy sites on a lattice with probability p,

29. K-core or bootstrap percolation
1) Occupy sites on a lattice with probability p,
2) If an occupied site has fewer than K occupied neighbors, empty it.

30. K-core or bootstrap percolation
1) Occupy sites on a lattice with probability p,
2) If an occupied site has fewer than K occupied neighbors, empty it.

31. K-core or bootstrap percolation
1) Occupy sites on a lattice with probability p,
2) If an occupied site has fewer than K occupied neighbors, empty it.

32. K-core or bootstrap percolation
1) Occupy sites on a lattice with probability p,
2) If an occupied site has fewer than K occupied neighbors, empty it.

33. K-core or bootstrap percolation
1) Occupy sites on a lattice with probability p,
2) If an occupied site has fewer than K occupied neighbors, empty it.

34. K-core or bootstrap percolation
1) Occupy sites on a lattice with probability p,
2) If an occupied site has fewer than K occupied neighbors, empty it.

35. K-core or bootstrap percolation
1) Occupy sites on a lattice with probability p,
2) If an occupied site has fewer than K occupied neighbors, empty it.

36. K-core or bootstrap percolation
1) Occupy sites on a lattice with probability p,
2) If an occupied site has fewer than K occupied neighbors, empty it.

37. K-core or bootstrap percolation
1) Occupy sites on a lattice with probability p,
2) If an occupied site has fewer than K occupied neighbors, empty it.

38. K-core or bootstrap percolation
1) Occupy sites on a lattice with probability p,
2) If an occupied site has fewer than K occupied neighbors, empty it.
Particles remain only if they have enough neighbors
“Coordination number” is discontinuous at transition (similar phenomenology to number of contacts at jamming transition)

39. Questions
How related are the dynamical phase transition in p-spin spin glasses and the K-core percolation transition?
Do these models contain the essential physics underlying the behavior of structural glasses and/or granular materials?

40. Applications of ideas from phase transitions to problems in computational complexity
SAT-unSAT transition for problems chosen from a random ensemble exhibits a phase transition that obeys scaling (Selman & Kirkpatrick)
Cavity method from spin glasses can be used to characterize SAT-unSAT transition (Biroli, Mézard, Monasson, Parisi)
phase transition within SAT region in which solution space breaks up into disconnected clusters (replica symmetry-breaking)

41. Is the renormalization group useful for studying problems in computational complexity?
Renormalization group gives insight into “easy-hard” transition in satisfiability problems
Renormalization approach to P versus NP question
Given Boolean function f(x1,x2,…,xN)
f(x1,x2,…,xN)  f(0,x2,…,xN)  f(1,x2,…,xN)
transforms function of N variables into one of N-1 variables

42. Renormalization group approach to characterizing P (problems that can be solved in polynomial time)
f(x1,x2,…,xN)  f(0,x2,…,xN)  f(1,x2,…,xN)
P is not a phase, but functions in P are either in or close to non-generic phases
all Boolean functions
low order polynomials
majority
xi mod 3
functions in P that are close to low order polynomials

43. Summary
Phase transitions and scale invariance have proven to be useful concepts for nonequilibrium systems, but general theoretical understanding is lacking
Glasses and granular materials may have deep similarities, but general theoretical understanding is lacking