Monte-Carlo-simulation

This repo contains the code used for Monte Carlo Simulation in DBT molecules using trained machine learning model

Electron trajectory

Mean Squared Displacement

Installation

First clone the repo

git clone https://github.com/ChunHou20c/Monte-Carlo-simulation.git

then create an virtual environment for the project

python3 -m venv ./env

activate the virtual environment

source activate ./env/bin/activate
#depends on your shell the environment file might be different

for windows:

.\env\scripts\activate

install all the requirement packages

pip install -r requirements.txt

Usage

Run the script run_simulation.py

python run_simulation.py

Modifications of cache path

The simulation will create cache file which stores all the coulombic matrix automatically when the simulation object is first constructed. This will reduce the simulation time when the simulation need to be run again.

By default the cache files are located in cache directory.

Modify CACHE_PATH in .env file to change the path for the cache

Running simulation for different molecule

The simulation can be run for 3 molecules: DBT1, DBT2 and DBT3 as defined in simulation package **The complete script is the same as in run_simulation.py, the parts may be modified are shown below

import the required packages

from simulation import simulation
import pandas as pd
from simulation import DBT1 # you may change to DBT2 or DBT3 
import os
from decouple import config

If you want to run for DBT2 or DBT3, make sure the molecule type and prediction model are also for DBT2 and DBT3

Define the simulation class

Simulation = simulation.Define_simulation_model('cnn_dbt1') # change cnn_dbt1 to use random model and for DBT2 and DBT3

options:

• cnn_dbt1 - trained cnn model for dbt1 molecule using tensorflow
• random_model_dbt1 - random model for dbt1
• random_model_dbt2 - random model for dbt2
• random_model_dbt3 - random model for dbt3

** There are only random model available for DBT2 and DBT3 molecule

Construct simulation object

test_sim = Simulation('Frames/DBT1-static.gro', DBT1.DBT1, memory_saving=True, cache_path=config('CACHE_PATH'))

This is the part that will affected by .env file

Create a path to store the results

path = "result_dbt1"
# Check whether the specified path exists or not
isExist = os.path.exists(path)
if not isExist:

   # Create a new directory because it does not exist
   os.makedirs(path)

Running the simulation

for i in range(10): #run for 10 times
    distance_list, time_list = test_sim.run()
    data_to_save = pd.DataFrame(list(zip(distance_list, time_list)), columns=['distance', 'time'])
    data_to_save.to_csv(f'{path}/sim_result_{i}.csv')

The result file

The result files are csv file that records all the displacement in nanometer of electron at time in second