https://github.com/mpharrigan/quant-accel

Quantitative analysis of msmaccelerator
https://github.com/mpharrigan/quant-accel

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Quantitative analysis of msmaccelerator

• Host: GitHub
• URL: https://github.com/mpharrigan/quant-accel
• Owner: mpharrigan
• Created: 2013-11-12T21:04:55.000Z (almost 11 years ago)
• Default Branch: master
• Last Pushed: 2022-10-13T04:13:09.000Z (about 2 years ago)
• Last Synced: 2023-08-02T04:58:53.937Z (over 1 year ago)
• Language: Python
• Size: 247 MB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
MAccelerator: Quantitative Analysis of Adaptive Sampling for MSMs

==================================================================

MSMs model the dynamics of a system by discritizing conformational space

into a finite number of states and counting the number of transitions between

each state. These raw counts are used to estimate reversible transition

probabilities. Theoretically, these raw counts can also be used to estimate

uncertainty in the model and select optimal states from which we can spawn

additional simulation. In practice, many design choices can affect the

speed-up gained from adaptive sampling.

## Tunable parameters for investigation

 - :white_check_mark: Adaptive frequency -- length of each trajectory before

                      re-starting simulation

 - :white_check_mark: Degree of Parallelization -- Number of trajectories

                      to run simultaneously

 - :white_circle:     Adaptive lag-time -- The system has a natural lag-time

                      for converged models. Would it help to use a different

                      lag-time for generating the counts matrix from which

                      we adapt?

## Adaptive schemes

 - :white_check_mark: Uniform -- Select a new state at random.

 - :white_circle:     Min-Counts -- Sort states by counts and choose states

                      in order

 - :white_circle:     Weighted-Counts -- Select new states with a probability

                      as counts^(-n) where n parameterizes exploration vs.

                      refinement

## Toy systems

 - :white_check_mark: Simulating from a known transition matrix

    - 20 state alanine dipeptide

 - :white_circle:     OpenMM simulation

    - Muller potential

# Software Design

This package takes a very object-oriented approach in its design. Each

combination of [toy system, adaptive scheme] is enclosed in a 

`Configuration` object. Each `Configuration` has a

- Method that yields a number of `AdaptiveParam`

  objects which contain tunable parameters.

- `Simulator` object

- `Modeller` object

- `ConvergenceChecker` object

- `Adapter` object

## Installing

Running

```

python setup.py install

```

will generate reference data and

install all relevant code. 

Requires Python 3.4+. Porting to older versions of python is unlikely

due to extensive use of new language features.

## Running

the script `maccel.py` can be used via command line to generate

- A sample python configuration script

- A sample job script to be used for `qsub`

The python script controlls any final details of a system before running.

For example, it makes sense to define the "grid" of tunable parameters

in this configuraiton script. The adaptive scheme will probably be

specified here as well, although right now it is just taken to be the default.