معرفی شرکت ها
adaptive-sampling-2.0.3
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Sampling algorithms for molecular transitions
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | adaptive-sampling-2.0.3 |
| نام | adaptive-sampling |
| نسخه کتابخانه | 2.0.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Andreas Hulm |
| ایمیل نویسنده | andreas.hulm@cup.uni-muenchen.de |
| آدرس صفحه اصلی | https://github.com/ochsenfeld-lab/adaptive_sampling |
| آدرس اینترنتی | https://pypi.org/project/adaptive-sampling/ |
| مجوز | MIT |
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:
```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 = 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:
```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,
)
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. 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>
نحوه نصب
نصب پکیج whl adaptive-sampling-2.0.3:
pip install adaptive-sampling-2.0.3.whl
نصب پکیج tar.gz adaptive-sampling-2.0.3:
pip install adaptive-sampling-2.0.3.tar.gz