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Exciting New Insights into Strongly Correlated
Oxides with Advanced Computing: Solving a Microscopic Model for
High Temperature Superconductivity
T. Maier, J. B. White, T. C. Schulthess (ORNL)M. Jarrell (University of Cincinnati)
P. Kent (UT/JICS & ORNL)
What is superconductivity
Outline of this Talk
A model for high temperature superconductors
t
U
Algorithm and leadership computing
1 2
211
1 1 22
2
2 1
New scientific insights and new opportunities
Electric Conduction in Normal Metals
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are needed to see this picture.
QuickTime™ and aVideo decompressor
are needed to see this picture.
In a perfect crystal
perfect conductor at T=0K
Real materials have defects
resistance finite at T=0K
at very low temperature metalscould become insulators (?)(proposal by Kelvin, 1902)
While verifying Kelvin’s theory, Kamerling Onnes discovers superconductivity in Hg at 4K
Resistance in pure mercury (Hg)drops to zero at liquid He temp.
Kamerling Onnes first to produceliquid helium (He) in 1908(Nobel prize in 1913)
Superconductor repels magnetic fieldMeissner and Ochsenfeld, Berlin 1933
Superconducting state is a new phase with zero resistance and perfect diamagnetism
BCS Theory or “normal” superconductors:Physical Review (1957), awarded Novel Prize in 1972
1950s: Bardeen Cooper and Schrieffer develop the theory of (conventional) superconductors
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QuickTime™ and aGIF decompressor
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Phonon mediated attractive interaction: formation of Cooper Pairs
Coherence length of Cooper Pairs is ~ 10-4 cm
Superconducting state: Cooper Pairs condense into macroscopic quantum state~ 1023 particles are coherent!
But, at T>25K, lattice vibration destroy Cooper Pairs: fundamental upper limit for TTcc
In 1986, Bednorz and Müller discover superconductivity in La5-xBaxCu5O5(3-y)
La5-xBaxCu5O5(3-y) with x=.75 has Tc~30K, normal state is poor conductorParent compound, LaCuO2, is an insulator!(Bednorz and Müller, Z. für Physik 1986, Nobel Prize 1987)
Something other than phonon mediate the formation of Cooper Pairs
Why modeling high temperature super-conductors is a challenge
• We have to account for a macroscopic number of particles
• The particles are correlated over several nanometers (from measured antiferromagnetic fluctuations)
• We need the many-body wave function or Green’s function (electron density and density functional theory not adequate)
The plan is to create a model that can be solved computationally
The complex structure of high temperature superconductors and where things happen
Cu O
O
Cu
O
Cu O Cu
O O
O
O
O Cu
O
O Cu
O
O O O
Cu O CuO O Cu
O O O
From experiment: superconductivity originates from 2-D CuO2 planes
Heavy ion (La, Y, Ba, Hg, ...) doping add / remove electrons to CuO2 planes
Doping with holes (electrons) leads to formation so-called Zhang-Rice singlet (Phys. Rev. B, 1988)
tU
Cu O
O
Cu
O
Cu O Cu
O O
O
O
O Cu
O
O Cu
O
O O O
Cu O CuO O Cu
O O O
Map onto a simple one-band and 2-D Hubbard model:
The cuprate high temperature superconductors are complex in a canonical way
Complex crystal structure Canonical phase diagram of the cpurates
superconductingAF
t
USimple model
can the simple 2D Hubbard model describe such rich physics?
The one-band 2D Hubbard model may be simple, but no simple solution is known for superconductivity!
Outline of the Dynamical Cluster Approximation (DCA)
Infinite lattice Cluster coupled tomean-field
DCA
•Short-ranged correlations within cluster treated explicitly
•Longer length scales treated on mean-field level
The key idea of the DCA is to systematically coarse-grain the self-energy (Jarrell et al., Phys. Rev. B 1998 ...)
Kinetic energy: Interaction energy:
Treated exactly in infinite system
Cut off correlations beyond cluster
K
k~
kx
2πL
ky
L
First Brillouin Zonecoarse grain self-energy:
What the DCA accomplishes in a nutshell:
Non-local correlations
Thermodynamic limit
Cluster in reciprocal space
Translational symmetry
QMC
We use Quantum Monte Carlo (QMC) to solve the many-body problem on the cluster
QMC-DCA generated phase diagram using a 2x2 cluster (calculations done on IBM P4 @ CCS and Compaq @ PSC)
d-wavesuperconductingAF
Issue: no antiferromagnetic (AF) transition in a strictly 2D model
Consequence of small (4-site) cluster:Need to study larger clusters!
Increasing the cluster size leads to performance problems on scalar processors
G
warm up sample QMC time
(dger)
warm up
G G
warm up
G
warm up
Workhorses of the QMC-DCA code are DGER and DGEMM, hence, we analyze DGER
DGER Performance (N=4480)
0
500
1000
1500
2000
2500
3000
Cray X1 SGI Altix IBM p690
Performance of Concurrent DGERs
(N=4480)
10
100
1000
10000
1 2 3 4 5 6 7 8
Processors (MSPs)
Cray X1
SGI Altix
IBM p690
N=4480 is a typical problem size for ~20 site DCA cluster
This translates into about an order of magnitude increase in performance on the Cray
DCA-QMC Runtime
0
10000
20000
30000
40000
50000
Problem Size
IBM p690, 8 PEs IBM p690, 32 PEs
Cray X1, 8 MSPs Cray X1, 32 MSPs
Code runs at 30-60% efficiency and scales to > 500 MSPs on the Cray X1
On the Cray X1 @ CCS we can simulate large enough clusters to validate the DCA algorithm
No antiferromagnetic order in 2D (Mermin Wagner Theorem)
Neel temperature (TN) indeed vanishes logarithmically
1 2
211
1 1 22
2
2 1
1 2
34
1
1
2
2
3
3
4
4
Nc=2:1
neighbor
Nc=4:2
neighbors
Take a closer look at the Nc=2 and 4 cases
Pay attention when running larger clusters to study the superconducting transition
• Problem:
- d-wave order parameter non-local (4 sites)
- Expect large size and geometry effects in small clusters
+-
+-
8A
16B
16A
Zd=1 Zd=2 Zd=3
Number of independent neighboring d-wave plaquettes:
Cluster Zd
4A 0(MF)
Superconducting transition is where the d-wave pair-field susceptibility (Pd) diverges
8A 112A 216B
216A 320A 4
24A 426A 4
Tc ≈ 0.025tSecond neighbor shell difficult
due to QMC sign problem
Superconductivity can be a consequence of strong electron correlations
What next?
• Materials specific model: try to understand the differences in Tc for different Cuprates (La vs. Hg based compounds)
- use input band structure from density functional ground state calculations
- explore better functionals than LDA, for example LDA+U or SIC-LSD
• Analyze and understand the pairing mechanism
• Analyze convergence of DCA algorithm
- central problem in order to obtain analytic Green’s functions!
- uniform convergence has been proved for cluster size 1, what about Nc>1?
• Develop a multi-scale DCA approach
- QMC sign problem WILL limit maximum cluster size and parameter range!
- different approximations of the self-energy at different length and time scales
Summary / Conclusions
• Superconductivity, a macroscopic quantum effect
• 2-D Hubbard model for strongly correlated high temperature superconducting cuprates
• Dynamical Cluster Approximation, QMC-DCA code, and the impact of the Cray X1 @ CCS to solve the 2-D Hubbard model
• We can model phase diagram of the cuprates microsopically
Superconductivity can be a result of strong electron correlations
This research used resources of the Center for Computational Sciences and was sponsored in part by the offices Basic Energy Sciences and of Advance Scientific Computing Research, U.S. Department of Energy. The work was performed at Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725. Work at Cincinnati was supported by the NSF Grant No. DMR-0113574.
Acknowledgement