Relative path

../fullrmc/Examples/MLSelection/

Description

A cubic box of 729 tetrahydrofuran molecules which makes a total of 9477 atoms is used to demonstrate that reinforcement machine learning can improve ratio of accepted moves by smartly selecting groups. This example was used to generate the following reinforcement machine learning selection images. It is very interesting to note that by zooming at the very beginning of the simulation, a warming up steps are needed for the SmartRandomSelector to learn the system and therefore increase the number of accepted moves by selecting the right groups.

Files

1. thf.pdb: The initial configuration input file.
2. run.py: The script file used to run the simulation.

run.py explained

IMPORTING USEFUL DEFINITIONS:

All useful packages, modules and definitions are imported.

SHUT DOWN LOGGING

Shut down all standard output logging. Set the level of logging to the maximum but force ‘move accepted’ logging file flag to be always True.

CREATE ENGINE:

Create the engine and the needed constraints. Set TranslationGenerator to all groups but 75% of those translation generators are given a realistic maximum translation amplitude of \(0.3 \AA\) while the remaining 25% are given an unrealistic maximum amplitude of \(10 \AA\) that will lead to a lot of unaccepted moves.

DIFFERENT RUNS:

Define functions to run the normal and machine learning enabled selection tests. All of the following functions finish running the engine using Engine.run method.

1. normal_run: Running engine with a normal and traditional random selector RandomSelector.
2. ML_run: Running engine with a reinforcement machine learning enabled random selector SmartRandomSelector.

RUN SIMULATION:

Run both normal_run and ML_run functions. Then flush the LOGGER to make sure everything is written to logging files.

READ LOGGING FILES DATA:

Read logged data and save the ratio of accepted moves to data files.

PLOT LOGGING DATA:

Plot the results.