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dqmc-parameter-scans
by edwnh
⭐ 16🍴 5📅 Dec 31, 2025
SKILL.md
name: dqmc-parameter-scans description: Set up systematic DQMC parameter studies across temperature, interaction strength U, or chemical potential mu. Use when doing temperature sweeps, phase diagram calculations, or any grid of simulations.
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:
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:
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
Score
Total Score
60/100
Based on repository quality metrics
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