82 lines
2.5 KiB
Markdown
82 lines
2.5 KiB
Markdown
[](https://travis-ci.org/danijoo/adaptiveumbrella)
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# Python module for adaptive umbrella sampling
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This module can be used to perform adaptive umbrella sampling of a multi-dimensional potential of mean force. The
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algorithm involves::
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1) calculate the free energy landscape
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2) Among existing windows, select windows with E < E_max
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3) For each selected window, generate 3^N-1 neighbor windows
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4) Sample new windows, then go to 1) or stop if no new windows can be found
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For more details about the algorithm, see
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Self-Learning Adaptive Umbrella Sampling Method for the Determination of Free Energy Landscapes in Multiple Dimensions (Wojtas-Niziurski, Meng, Roux, Bernèche, 2013)
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[(https://doi.org/10.1021/ct300978b)](https://doi.org/10.1021/ct300978b)
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## Usage
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Implement the UmbrellaRunner class according to your needs:
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```python
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from adaptiveumbrella.runner import UmbrellaRunner
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class MyUmbrellaRunner(UmbrellaRunner):
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pass
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```
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within the class we need to define two methods. First we have to define how the simulation windows should be sampled:
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```python
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def simulate_frames(self, lambdas, frames):
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""" Run simulations for all passed lambda steps. `lambdas` is a dictionary where each key
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is a tuple of coordinates in the phase space and each value are the lambda values of the root
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from which this frame should be created. `frames` is an identical dict, but with indeces of the pmf
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numpy array defining the phase space """
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pass
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```
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then, we have to implement a method that updates the pmf:
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```python
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def calculate_new_pmf(self):
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""" This is called after `simulate_frames` and should calculate the new PMF. return value must be a numpy
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array of similar dimensions then the lambda states.
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pass
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```
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Finally, we can instantiate the class, pass the configuration variables and start the simulations:
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```python
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runner = MyUmbrellaRunner()
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# 2 dimensional phase space ranging from -3 to 3 in both dimensions
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# lambda spacing is 0.2 in x and y
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runner.cvs = np.array([
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(-3, 3, 0.2),
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(-3, 3, 0.2),
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])
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# initial lambda coordinates
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runner.cvs_init = (1.4, -1.4)
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# initial energy for finding new frames
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runner.E_min = 10
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# max. energy before sampling stops
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runner.E_max = 100
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# energy change between steps
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runner.E_incr = 10
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# max. number of iterations before stopping
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runner.max_iterations = 100
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# let's go
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runner.run()
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```
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