Description:

adaptivesampling 3.0.1

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]


Accelerated MD (aMD), Gaussian accelerated MD (GaMD), Sigmoid Accelerated MD (SaMD) [7, 8, 9]


Gaussian-accelerated WTM-eABF [10]


Free-energy Nudged Elastic Band Method [11]


Implemented Collective Variables:


Distances, angles and torsion angles as well as linear combinations thereof


Coordination numbers


Minimized Cartesian RMSD (Kabsch algorithm)


Adaptive path collective variables (PCVs) [12, 13]


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 = the_bias.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,
device='cpu',
)

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:

Comer et al., J. Phys. Chem. B (2015); https://doi.org/10.1021/jp506633n
Lesage et al., J. Phys. Chem. B (2017); https://doi.org/10.1021/acs.jpcb.6b10055
Shirts et al., J. Chem. Phys. (2008); https://doi.org/10.1063/1.2978177
Hulm et al., J. Chem. Phys. (2022); https://doi.org/10.1063/5.0095554
Barducci et al., Phys. rev. lett. (2008); https://doi.org/10.1103/PhysRevLett.100.020603
Fu et al., J. Phys. Chem. Lett. (2018); https://doi.org/10.1021/acs.jpclett.8b01994
Hamelberg et al., J. Chem. Phys. (2004); https://doi.org/10.1063/1.1755656
Miao et al., J. Chem. Theory Comput. (2015); https://doi.org/10.1021/acs.jctc.5b00436
Zhao et al., J. Phys. Chem. Lett. (2023); https://doi.org/10.1021/acs.jpclett.2c03688
Chen et al., J. Chem. Theory Comput. (2021); https://doi.org/10.1021/acs.jctc.1c00103
Semelak et al., J. Chem. Theory Comput. (2023); https://doi.org/10.1021/acs.jctc.3c00366
Branduardi, et al., J. Chem. Phys. (2007); https://doi.org/10.1063/1.2432340
Leines et al., Phys. Ref. Lett. (2012); https://doi.org/10.1103/PhysRevLett.109.020601

This and Related Work:
If you use this package in your work please cite:

Hulm et al., J. Chem. Phys., 157, 024110 (2022); https://doi.org/10.1063/5.0095554

Other related references:

Dietschreit et al., J. Chem. Phys., (2022); https://aip.scitation.org/doi/10.1063/5.0102075
Hulm et al., J. Chem. Theory. Comput., (2023); https://doi.org/10.1021/acs.jctc.3c00938
Stan et. al., ACS Cent. Sci., (2024); https://doi.org/10.1021/acscentsci.3c01403

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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