.. _chap-tutorials:

Tutorials
############

=================================================================
Creating (Training) A Machine Learning Potential With Abinit Data
=================================================================
Go to the **testing_data** directory inside the **mlmd** directory 
containing the *setup.py* file. Inside **testing_data** there are three 
directories (mlmd_data_test_abinit , mlmd_data_test_fireball, mlmd_data_test_vasp), 
go inside the **mlmd_data_test_abinit** directory, inside this directory, 
there are three files (input.md, input.stru, training.in), and one 
directory (dft_data ). To train the machine learning potential, *mlmd_* 
needs (see :ref: `chap-mlp`) the **input.stru** file containing the 
information for the machine learning potential (see :ref: `chap-training_file`), 
and the **dft_data** directory containing the results of 
DFT calculations performed with abinit. 

To train a machine learning potential, execute the command: ::

	mlmd_ -mode 'create_potential' -training_file 'training.in'
The calculation may take some time depending on the processing power of 
the system carrying out the calculation, at the end of the calculation a 
directory named **potential_test**, this directory contains the machine 
learning potential. 

===================================================================
Creating (Training) A Machine Learning Potential With Fireball Data
===================================================================
Go to the **testing_data** directory inside the **mlmd** directory 
containing the *setup.py* file. Inside **testing_data** there are three 
directories (mlmd_data_test_abinit , mlmd_data_test_fireball, mlmd_data_test_vasp), 
go inside the **mlmd_data_test_fireball** directory, inside this directory, 
there are three files (input.md, input.stru, training.in), and one 
directory (dft_data ). To train the machine learning potential, *mlmd_* 
needs (see :ref: `chap-mlp`) the **input.stru** file containing the 
information for the machine learning potential (see :ref: `chap-training_file`), 
and the **dft_data** directory containing the results of 
DFT calculations performed with fireball. 

To train a machine learning potential, execute the command: ::

	mlmd_ -mode 'create_potential' -training_file 'training.in'

The calculation may take some time depending on the processing power of 
the system carrying out the calculation, at the end of the calculation a 
directory named **potential_test**, this directory contains the machine 
learning potential. 

===============================================================
Creating (Training) A Machine Learning Potential With Vasp Data
===============================================================

Go to the **testing_data** directory inside the **mlmd** directory 
containing the *setup.py* file. Inside **testing_data** there are three 
directories (mlmd_data_test_abinit , mlmd_data_test_fireball, mlmd_data_test_vasp), 
go inside the **mlmd_data_test_vasp** directory, inside this directory, 
there are three files (input.md, input.stru, training.in), and one 
directory (dft_data ). To train the machine learning potential, *mlmd_* 
needs (see :ref: `chap-mlp`) the **input.stru** file containing the 
information for the machine learning potential (see :ref: `chap-training_file`), 
and the **dft_data** directory containing the results of 
DFT calculations performed with vasp. 

To train a machine learning potential, execute the command: ::

	mlmd_ -mode 'create_potential' -training_file 'training.in'

The calculation may take some time depending on the processing power of 
the system carrying out the calculation, at the end of the calculation a 
directory named **potential_test**, this directory contains the machine 
learning potential. 

=============================================================
Performing Molecuar Dyanmics With A Machine Leaning Potential
=============================================================

Go to the **testing_data** directory inside the **mlmd** directory 
containing the *setup.py* file. Inside **testing_data** there are three 
directories (mlmd_data_test_abinit , mlmd_data_test_fireball, mlmd_data_test_vasp), 
select whichever of those three as long as you already made a machine 
learning potential in choosen one.

To perform Molecular Dynamics with *mlmd_* you need two files 
*the structure file* (:ref: `chap-stru_file`) and *the md file* 
(:ref: `chap-md_file`),  in addition you need a machine learning 
potential (:ref: `chap-mlp`).

To make *mlmd_* performs Molecular Dynamics execute the command ::
	
	mlmd_ -mode 'perform_md' -potential_dir 'potential_test' -structure_file 'input.stru' -md_file 'input.md'

The result of the Molecular Dynamics a xyz file with the trayectory of 
the structure undergoing the Molecular Dynamics.

====================================================
Calculate The Filter Behler Parrinello Features Only
====================================================

To calculate the Filter Behler Parrinello (FBP) features only, you can 
use the Jupyter Notebooks stored in **mlmd/testing_data/jupyter_notebooks**, 
there are four notebooks (one per every  DFT code supported, and one extra 
where the inputs are only xyz files).

In case you want to calculate the FBP features for a structure or many 
structures without processing them through a DFT code, you can do it by 
putting the structures in a .xyz file (the energy and temperature can be 
0.00, it does not matter for the feature calculation), once the structures 
are in a .xyz file format you can use the *feature_calulation_from_xyz.ipynb* 
notebook to see how to obtain the FBP features.