PESampler¶

class snap_simulate.hoomd.PESampler(logger, file_name='pe_sampler.pkl', sampling_mode='linear', min_interval=10, max_interval=None, Nmag=10, Navg=1, use_multiprocessing=False, auto_load=True)[source]¶: Potential Energy autocorrelation sampler.

The PESampler class inherits from BaseSampler and measures the autocorrelation function of the system’s potential energy (PE) using Fast Fourier Transforms (FFT).

Overview¶

PESampler periodically extracts potential energy values logged by a HOOMD-blue thermodynamic compute. At each evaluation interval, it calculates the autocorrelation of potential energy fluctuations:

\[C_{PE}(t) = \frac{\langle (U(t') - \langle U \rangle)(U(t' + t) - \langle U \rangle) \rangle}{\sigma_U^2}\]

When the trailing average over tail_window sample points drops below threshold, minimize_autocorrelation() stops the simulation.

Parameters¶

logger (hoomd.logging.Logger): A HOOMD-blue logger instance configured to log the thermodynamic potential energy (e.g. from ThermodynamicQuantities).
**kwargs: All additional keyword arguments (file_name, sampling_mode, min_interval, max_interval, Nmag, Navg, use_multiprocessing, auto_load) are inherited directly from BaseSampler. See BaseSampler for parameter details. (Note that file_name defaults to "pe_sampler.pkl" for PESampler).

Usage Example¶

import hoomd
from snap_simulate.hoomd import PESampler, minimize_autocorrelation

# Set up thermodynamic quantities compute
thermo = hoomd.md.compute.ThermodynamicQuantities(filter=hoomd.filter.All())
sim.operations.computes.append(thermo)

# Configure HOOMD logger for potential energy
logger = hoomd.logging.Logger()
logger.add(thermo, quantities=["potential_energy"])

# Create PE sampler with logarithmic scheduling and multiprocessing
sampler = PESampler(logger, sampling_mode="logarithmic", use_multiprocessing=True)

# Run until potential energy decorrelates
result = minimize_autocorrelation(sim, sampler, threshold=0.01, max_steps=500000)
print(f"Decorrelation achieved: {result['minimized']}")