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:
```shell
$ 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:
```python
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:
```python
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:
```python
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:
```shell
$ 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. Hamelberg et al., J. Chem. Phys. (2004); <https://doi.org/10.1063/1.1755656>
8. Miao et al., J. Chem. Theory Comput. (2015); <https://doi.org/10.1021/acs.jctc.5b00436>
9. Zhao et al., J. Phys. Chem. Lett. (2023); <https://doi.org/10.1021/acs.jpclett.2c03688>
10. Chen et al., J. Chem. Theory Comput. (2021); <https://doi.org/10.1021/acs.jctc.1c00103>
11. Semelak et al., J. Chem. Theory Comput. (2023); <https://doi.org/10.1021/acs.jctc.3c00366>
12. Branduardi, et al., J. Chem. Phys. (2007); <https://doi.org/10.1063/1.2432340>
13. 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>
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"description": "Adaptive Sampling\n=================\n\nThis package implements various sampling algorithms for the calculation of free energy profiles of molecular transitions. \n\n## Available Sampling Methods Include:\n*\tAdaptive Biasing Force (ABF) method [1] \n\t\n* \tExtended-system ABF (eABF) [2]\n\n\t* On-the-fly free energy estimate from the Corrected Z-Averaged Restraint (CZAR) [2]\n\t\n\t* Application of Multistate Bannett's Acceptance Ratio (MBAR) [3] to recover full statistical information in post-processing [4]\n\t\n* \t(Well-Tempered) Metadynamics (WTM) [5] and WTM-eABF [6]\n\n* \tAccelerated MD (aMD), Gaussian accelerated MD (GaMD), Sigmoid Accelerated MD (SaMD) [7, 8, 9]\n\n*\tGaussian-accelerated WTM-eABF [10]\n\n* Free-energy Nudged Elastic Band Method [11]\n\n## Implemented Collective Variables:\n\n* Distances, angles and torsion angles as well as linear combinations thereof\n\n* Coordination numbers \n\n* Minimized Cartesian RMSD (Kabsch algorithm)\n\n* Adaptive path collective variables (PCVs) [12, 13]\n\n## Install:\nTo install adaptive_sampling type:\n```shell\n$ pip install adaptive-sampling\n```\n\n\n## Requirements:\n* python >= 3.8\n* numpy >= 1.19\n* torch >= 1.10\n* scipy >= 1.7\n\n## Basic Usage:\nTo 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:\n\n```python\nclass MD:\n # Your MD code\n ...\n\n def get_sampling_data(self):\n try:\n from adaptive_sampling.interface.sampling_data import SamplingData\n\n mass = ...\n coords = ...\n forces = ...\n epot = ...\n temp = ...\n natoms = ...\n step = ...\n dt = ...\n\n return SamplingData(mass, coords, forces, epot, temp, natoms, step, dt)\n except ImportError as e:\n raise NotImplementedError(\"`get_sampling_data()` is missing `adaptive_sampling` package\") from e\n```\nThe bias force on atoms in the N-th step can be obtained by calling `step_bias()` on any sampling algorithm:\n```python\nfrom adaptive_sampling.sampling_tools import *\n\n# initialize MD code\nthe_md = MD(...)\n\n# collective variable\natom_indices = [0, 1] \nminimum = 1.0 # Angstrom\nmaximum = 3.5 # Angstrom\nbin_width = 0.1 # Angstrom \ncollective_var = [[\"distance\", atom_indices, minimum, maximum, bin_width]]\n\n# extended-system eABF \next_sigma = 0.1 # thermal width of coupling between CV and extended variable in Angstrom\next_mass = 20.0 # mass of extended variable \nthe_bias = eABF(\n ext_sigma, \n ext_mass, \n the_md, \n collective_var, \n output_freq=10, \n f_conf=100, \n equil_temp=300.0\n)\n\nfor md_step in range(steps):\n # propagate langevin dynamics and calc forces \n ... \n bias_force = the_bias.step_bias(write_output=True, write_traj=True)\n the_md.forces += bias_force\n ...\n # finish md_step\n```\nThis automatically writes an on-the-fly free energy estimate in the output file and all necessary data for post-processing in a trajectory file.\nFor extended-system dynamics unbiased statistical weights of individual frames can be obtained using the MBAR estimator:\n```python\nimport numpy as np\nfrom adaptive_sampling.processing_tools import mbar\n\ntraj_dat = np.loadtxt('CV_traj.dat', skiprows=1)\next_sigma = 0.1 # thermal width of coupling between CV and extended variable \n\n# grid for free energy profile can be different than during sampling\nminimum = 1.0 \nmaximum = 3.5 \nbin_width = 0.1 \ngrid = np.arange(minimum, maximum, bin_width)\n\ncv = traj_dat[:,1] # trajectory of collective variable\nla = traj_dat[:,2] # trajectory of extended system\n\n# run MBAR and compute free energy profile and probability density from statistical weights\ntraj_list, indices, meta_f = mbar.get_windows(grid, cv, la, ext_sigma, equil_temp=300.0)\n\nexp_U, frames_per_traj = mbar.build_boltzmann(\n traj_list, \n meta_f, \n equil_temp=300.0,\n)\n\nweights = mbar.run_mbar(\n exp_U,\n frames_per_traj,\n max_iter=10000,\n conv=1.0e-7,\n conv_errvec=1.0,\n outfreq=100,\n device='cpu',\n)\n\npmf, rho = mbar.pmf_from_weights(grid, cv[indices], weights, equil_temp=300.0)\n```\n\n## Documentation:\nCode documentation can be created with pdoc3:\n```shell\n$ pip install pdoc3\n$ pdoc --html adaptive_sampling -o doc/\n```\n## References:\n1. Comer et al., J. Phys. Chem. B (2015); <https://doi.org/10.1021/jp506633n> \n2. Lesage et al., J. Phys. Chem. B (2017); <https://doi.org/10.1021/acs.jpcb.6b10055>\n3. Shirts et al., J. Chem. Phys. (2008); <https://doi.org/10.1063/1.2978177>\n4. Hulm et al., J. Chem. Phys. (2022); <https://doi.org/10.1063/5.0095554>\n5. Barducci et al., Phys. rev. lett. (2008); <https://doi.org/10.1103/PhysRevLett.100.020603>\n6. Fu et al., J. Phys. Chem. Lett. (2018); <https://doi.org/10.1021/acs.jpclett.8b01994>\n7. Hamelberg et al., J. Chem. Phys. (2004); <https://doi.org/10.1063/1.1755656>\n8. Miao et al., J. Chem. Theory Comput. (2015); <https://doi.org/10.1021/acs.jctc.5b00436>\n9. Zhao et al., J. Phys. Chem. Lett. (2023); <https://doi.org/10.1021/acs.jpclett.2c03688>\n10. Chen et al., J. Chem. Theory Comput. (2021); <https://doi.org/10.1021/acs.jctc.1c00103>\n11. Semelak et al., J. Chem. Theory Comput. (2023); <https://doi.org/10.1021/acs.jctc.3c00366>\n12. Branduardi, et al., J. Chem. Phys. 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