adaptive-sampling 2.0.2

Sampling algorithms for molecular transitions

Adaptive Sampling

This package implements various sampling algorithms for the calculation of free energy profiles of molecular transitions.

Available sampling methods include:

• Adaptive Biasing Force (ABF) method [1]

• Extended-system ABF (eABF) [2]

  • On-the-fly free energy estimate from the Corrected Z-Averaged Restraint (CZAR) [2]

  • Application of Multistate Bannett's Acceptance Ratio (MBAR) [3] to recover full statistical information in post-processing [4]

• Well-Tempered Metadynamics (WTM) [5] and WTM-eABF [6]

• Gaussian-accelerated MD (GaMD) [7] and GaWTM-eABF [8]

Install:

To install adaptive_sampling type:

$ pip install adaptive-sampling

Requirements:

• python >= 3.8
• numpy >= 1.19
• torch >= 1.10
• scipy >= 1.7

Basic Usage:

To use adaptive sampling with your MD code of choice add a function called get_sampling_data() to the corresponding python interface that returns an object containing all required data. Hard-coded dependencies can be avoided by wrapping the adaptive_sampling import in a try/except clause:

class MD:
    # Your MD code
    ...

    def get_sampling_data(self):
        try:
            from adaptive_sampling.interface.sampling_data import SamplingData

            mass   = ...
            coords = ...
            forces = ...
            epot   = ...
            temp   = ...
            natoms = ...
            step   = ...
            dt     = ...

            return SamplingData(mass, coords, forces, epot, temp, natoms, step, dt)
        except ImportError as e:
            raise NotImplementedError("`get_sampling_data()` is missing `adaptive_sampling` package") from e

The bias force on atoms in the N-th step can be obtained by calling step_bias() on any sampling algorithm:

from adaptive_sampling.sampling_tools import *

# initialize MD code
the_md = MD(...)

# collective variable
atom_indices = [0, 1] 
minimum   = 1.0  # Angstrom
maximum   = 3.5  # Angstrom
bin_width = 0.1  # Angstrom 
collective_var = [["distance", atom_indices, minimum, maximum, bin_width]]

# extended-system eABF 
ext_sigma = 0.1  # thermal width of coupling between CV and extended variable in Angstrom
ext_mass = 20.0  # mass of extended variable 
the_bias = eABF(
    ext_sigma, 
    ext_mass, 
    the_md, 
    collective_var, 
    output_freq=10, 
    f_conf=100, 
    equil_temp=300.0
)

for md_step in range(steps):
    # propagate langevin dynamics and calc forces 
    ... 
    bias_force = eABF.step_bias(write_output=True, write_traj=True)
    the_md.forces += bias_force
    ...
    # finish md_step

This automatically writes an on-the-fly free energy estimate in the output file and all necessary data for post-processing in a trajectory file. For extended-system dynamics unbiased statistical weights of individual frames can be obtained using the MBAR estimator:

import numpy as np
from adaptive_sampling.processing_tools import mbar

traj_dat = np.loadtxt('CV_traj.dat', skiprows=1)
ext_sigma = 0.1    # thermal width of coupling between CV and extended variable 

# grid for free energy profile can be different than during sampling
minimum   = 1.0     
maximum   = 3.5    
bin_width = 0.1    
grid = np.arange(minimum, maximum, bin_width)

cv = traj_dat[:,1]  # trajectory of collective variable
la = traj_dat[:,2]  # trajectory of extended system

# run MBAR and compute free energy profile and probability density from statistical weights
traj_list, indices, meta_f = mbar.get_windows(grid, cv, la, ext_sigma, equil_temp=300.0)

exp_U, frames_per_traj = mbar.build_boltzmann(
    traj_list, 
    meta_f, 
    equil_temp=300.0
)

weights = mbar.run_mbar(
    exp_U,
    frames_per_traj 
    max_iter=10000,
    conv=1.0e-7,
    conv_errvec=1.0,
    outfreq=100,
)

pmf, rho = mbar.pmf_from_weights(grid, cv[indices], weights, equil_temp=300.0)

Documentation:

Code documentation can be created with pdoc3:

$ pip install pdoc3
$ pdoc --html adaptive_sampling -o doc/

References:

1. Comer et. al., J. Phys. Chem. B (2015); https://doi.org/10.1021/jp506633n
2. Lesage et. al., J. Phys. Chem. B (2017); https://doi.org/10.1021/acs.jpcb.6b10055
3. Shirts et. al., J. Chem. Phys. (2008); https://doi.org/10.1063/1.2978177
4. Hulm et. al., J. Chem. Phys. (2022); https://doi.org/10.1063/5.0095554
5. Barducci et. al., Phys. rev. lett. (2008); https://doi.org/10.1103/PhysRevLett.100.020603
6. Fu et. al., J. Phys. Chem. Lett. (2018); https://doi.org/10.1021/acs.jpclett.8b01994
7. Miao et. al., J. Chem. Theory Comput. (2015); https://doi.org/10.1021/acs.jctc.5b00436
8. Chen et. al., J. Chem. Theory Comput. (2021); https://doi.org/10.1021/acs.jctc.1c00103

This and Related Work:

1. Hulm et. al., J. Chem. Phys. (2022); https://doi.org/10.1063/5.0095554
2. Dietschreit et al., J. Chem. Phys., 157, 084113 (2022).; https://aip.scitation.org/doi/10.1063/5.0102075