Parameter Scans
Generate a directory tree of simulation files (one directory per parameter point), then run with the queue system (see dqmc-run), then analyze (see dqmc-analyze).
Temperature Scan
Vary L while adjusting dt to maintain Trotter error bound:
python
from dqmc_util import gen_1band_hub
import numpy as np
U = 4.0
step = 5 # L must be divisible by n_matmul and period_eqlt (defaults: 5)
for T in [0.1, 0.2, 0.5, 1.0]:
beta = 1.0 / T
dt = min((0.05/U)**0.5, beta / 10)
L = int(np.ceil(beta / dt / step) * step)
dt = beta / L
gen_1band_hub.create_batch(
prefix=f"data/T{T:.2f}/bin",
Nfiles=4, Nx=6, Ny=6, U=U, dt=dt, L=L
)
U-mu Scan
Grid over interaction strength and chemical potential:
python
import itertools
import numpy as np
from dqmc_util import gen_1band_hub
dt, L = 0.1, 40 # sets beta = L*dt
for U, mu in itertools.product([2, 4, 6, 8], np.linspace(-4, 4, 9)):
gen_1band_hub.create_batch(
prefix=f"data/U{U}_mu{mu:.1f}/bin",
Nfiles=4, Nx=6, Ny=6, U=U, mu=mu, dt=dt, L=L
)
Validation
- • Directory structure created as expected
- • Each directory has correct number of
.h5files
Tips
- •Use descriptive directory names encoding key parameters
- •Keep
Nfiles >= 4for reliable error estimates - •For large scans, generate files first, then run via queue system