SPEMD

Successful Parameter Enumerator for MD simulation

Requirements

1. Python >= 2.7
2. COMBO: A Bayesian optimization library
3. numpy >= 1.15.0
4. scikit-learn >= 0.19.1
5. scipy >= 1.1.0

Installation

Download or clone the github repository, e.g. git clone https://github.com/tsudalab/SPEMD

Usage

• Preparation

  • Please list all parmeter candidates as a CSV file. (See example/parameter_list(Newtonian).csv or example/parameter_list(Langevin).csv)
  • Please call your MD simulation in the simulation function in simulator.py and return its success rate.

• Sucessful parameter enumeration based on machine learning algorithms. (See the example commands of F1-motor in the following.)

  • python parameter_enumerator.py [Comma separated numbers of candidate for each variable] [Number of sampling iterations] [Directory of the parameter candidate file] [Successful threshold] --method [Search method]
  • Available search methods: BOUS (Combination of BO and US), BO (Bayesian Optimization), US (Uncertainty Sampling), RS (Random Sampling)

Enumeration examples of F1-motor simulations based on CG-MD

• Newtonian dynamics version.

  • python parameter_enumerator.py 21,12 100 example/parameter_list\(Newtonian\).csv 1.0 --method BOUS --test Newtonian
  • BOUS based search with the success threshold of 1.0 using 100 samplings

• Langevin dynamics version.

  • python parameter_enumerator.py 30,9,5 400 example/parameter_list\(Langevin\).csv 0.8 --method BOUS --test Langevin
  • US based search with the success threshold of 0.8 using 400 samplings