Skip to content

Commit

Permalink
Update README.md
Browse files Browse the repository at this point in the history 
Demo restructuring
  • Loading branch information
@vasilsaroka
vasilsaroka authored Jun 26, 2023
1 parent 8fb767b commit db07ba8
Showing 1 changed file with 19 additions and 19 deletions.
38 changes: 19 additions & 19 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -10,23 +10,37 @@ Automate [Quantum Espresso](https://www.quantum-espresso.org/)(QE) routines
The code was developed and tested under
- OS: Linux Mint 21.1 Vera Xfce
- Python 3.10, with modules *os*, *subprocesses*, *re*, *numpy*, *time*, *datetime*, *matplotlib.pyplot*.
- PWSCF v.6.7MaX that is a part of [Quantum ESPRESSO](https://www.quantum-espresso.org/) suite with *Si.pz-vbc.UPF* pseudo potential.
- PWSCF v.6.7MaX that is a part of [Quantum ESPRESSO](https://www.quantum-espresso.org/) suite.

## Preliminaries
Under Linux OS:
- Install QE by typing in the terminal: ``sudo apt-get install quantumespresso`` (recommended) or see [QE](https://www.quantum-espresso.org/login/) web page.
- Install Python distribution by typing in the terminal: ``sudo apt-get install conda`` (recommended) or see [Anaconda Distribution](https://www.anaconda.com/download/#linux) guidelines.
- Pseudo potentials can be taken from [Pseudo Dojo](http://www.pseudo-dojo.org/).

## Demo
### Bash script DEopt
## Differential Evolution
This repository contains a Python code for Quantum Espresso parameters optimization with Differential Evolution (DE) global optimization method [1].
A general introduction into DE method together with a python realization can be found in the excellent [tutorial](https://pablormier.github.io/2017/09/05/a-tutorial-on-differential-evolution-with-python/#) by Pablo Rodriguez-Mier. Here, the DE method is adapted for the *geometry optimization* of the bulk Si crystal. In effect, this allows one to perform *0_Si_bulk* tasks *0_cutoff*, *1_alat* and *2_kpoints* from [DFT_QE_beginner_tutorial](https://github.com/Crivella/DFT_QE_beginner_tutorial) in a hassle-free single run.

- [1] Price, K., Storn, R. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. *Journal of Global Optimization*, **11**, 341–359. [https://doi.org/10.1023/A:1008202821328](https://doi.org/10.1023/A:1008202821328)

### Python
The DE algorithm here is rewritten from scratch in what we call a generation mixing form. It allows us to save memory by storing the parameter space vectors from different generation in the same array. Tests have shown that this does not affect the performance of the DE algorithm. In addition, the DE iterations stop when the convergence is achieved to avoid extra evaluations. In the given case, convergence is defined as the spread of the cost function values for all parameter space vectors being less than some threshold value.

The python file contains the main functions *QEGenrun*(cost function) and *QEDE*, and the cell for a test run of the code on bulk Si followed by a few other cells that are used for the output results analysis such as visualizing and animating the DE convergence. The *QEDE* can perform optimization for an arbitrary number of parameters including integer ones, i.e. those for which only integer part is meaningful. The integer parameters are set by int_param_num option and, by convention, they are being counted from the end of a parameter space vector. Therefore, integer parameters must be placed accordingly in the parameter limits and placeholders arrays. A template file for the bulk Si with parameter placeholders starting by convention from '@' is provided.

### Bash
This repository also contains a bash script that creates the template of [QE input file](https://www.quantum-espresso.org/Doc/INPUT_PW.html) and a python script and then runs the python script. Once the python script run is finished, the temporarily created files are deleted leaving only the resulting QEDE*.out and QEDE*.png output files. The bulk Si is taken as an example with the three optimization parameters: ecutwfc, celldm and kpt. **For kpt parameter only its integer part is meaningful**.

### Demo
#### Bash script DEopt
- Put Bash script ``DEopt`` and *Si.pz-vbc.UPF* pseudo potential into the same folder, i.e. the working folder.
- Open terminal in this folder and run the Bash script in terminal as ``./DEopt``.
- Find in the working folder the QE outdir ``./tmp``.
- Find in the working folder the results of the optimization and convergence plot in *QEDE_year-month-day_hours_minutes_seconds.out* and *QEDE_year-month-day_hours_minutes_seconds.png*.

### Python file DEopt.py
- Put Python file ``DEopt.py`` with with QE input file *Si.scf-template.in* and pseudo potential *Si.pz-vbc.UPF* into the same folder, i.e. working folder
#### Python file DEopt.py
- Put Python file ``DEopt.py`` with QE input file *Si.scf-template.in* and pseudo potential *Si.pz-vbc.UPF* into the same folder, i.e. working folder
- Open Python file and evaluate it cell after cell sequentially.
- Find in the working folder the QE outdir ``./tmp``.
- Find in the working folder the results of optimization and convergence plot in *QEDE_year-month-day_hours_minutes_seconds.out* and *QEDE_year-month-day_hours_minutes_seconds.png*.
Expand All @@ -37,20 +51,6 @@ Under Linux OS:
![animation_Si](https://github.com/vasilsaroka/QEskillbox/assets/49445896/bd93e31e-657b-4ebc-9297-88b84fbc5efb)


## Differential Evolution
This repository contains a Python code for Quantum Espresso parameters optimization with Differential Evolution (DE) global optimization method [1].
A general introduction into DE method together with a python realization can be found in the excellent [tutorial](https://pablormier.github.io/2017/09/05/a-tutorial-on-differential-evolution-with-python/#) by Pablo Rodriguez-Mier. Here, the DE method is adapted for the *geometry optimization* of the bulk Si crystal. In effect, this allows one to perform *0_Si_bulk* tasks *0_cutoff*, *1_alat* and *2_kpoints* from [DFT_QE_beginner_tutorial](https://github.com/Crivella/DFT_QE_beginner_tutorial) in a hassle free single run.

- [1] Price, K., Storn, R. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. *Journal of Global Optimization*, **11**, 341–359. [https://doi.org/10.1023/A:1008202821328](https://doi.org/10.1023/A:1008202821328)

### Python
The DE algorithm here is rewritten from scratch in what we call a generation mixing form. It allows us to save memory by storing the parameters space vectors from different generation in the same array. Tests have shown that this does not affect the performance of the DE algorithm. In addition, the DE iterations stop when the convergence is achieved to avoid extra evaluations. In the given case, convergence is defined as the spread of the cost function values for all parameter space vectors being less than some threshold value.

The python file contains the main functions *QEGenrun*(cost function) and *QEDE*, the cell for a test run of the code on bulk Si followed by a few other cells that are used for the output results analysis such as visualizing and animating the DE convergence. The *QEDE* can perform optimization for an arbitrary number of parameters including integer ones, i.e. those for which only integer part is meaningful. The integer parameters are set by int_param_num option and, by convention, they are being counted from the end of a parameter space vector. Therefore, integer parameters must be placed accordingly in the parameter limits and placeholders arrays. A template file for the bulk Si with parameter placeholders starting by convention from '@' is provided.

### Bash
This repository also contains a bash script that creates the template of [QE input file](https://www.quantum-espresso.org/Doc/INPUT_PW.html) and a python script and then runs the python script. Once the python script run is finished, the temporarily created files are deleted leaving only the resulting QEDE*.out and QEDE*.png output files. The bulk Si is taken as an example with the three optimization parameters: ecutwfc, celldm and kpt. **For kpt parameter only its integer part is meaningful**.

## Supporting this project
We believe everyone deserves access to knowledge that is grounded in science and integrity. That is why we keep our code open for all users, regardless of where they live or what they can afford to pay. This means more people can be better educated and inspired to make an impact on the global wellbeing. We have no shareholders or billionaire owner, meaning only your donations power our work and ensure it can remain open for all. Every contribution, however big or small, makes a real difference for QEskillbox future. If you find it useful, consider [supporting the project](https://paypal.me/vasilsaroka?locale.x=en_GB)
.

0 comments on commit db07ba8

Please sign in to comment.