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A stable version of the module can be downloaded using the download button on this `page <https://gitlab.com/PANNAdevs/panna>`_
As a python module PANNA-GVECT does not require installation but it relies on numpy library version > 1.15.0.
As a python module PANNA-GVECT does not require installation but it relies on numpy library version >= 1.15.0.
In order to set up and test the module, run the following::
......@@ -60,7 +60,7 @@ PANNA-GVECT main script requires a configuration file that specifies the paramet
A typical command for using this module is as follows::
$ export PYTHONPATH=/path/to/panna/directory/panna
$ python3 gvect_calculator.py -ini gvect_configuration.ini
$ python3 gvect_calculator.py --config gvect_configuration.ini
A detailed tutorial about the contents of the configuration file can be found
`here <https://gitlab.com/PANNAdevs/panna/blob/master/doc/tutorial/README_tutorial_2_data_preparation.md>`_.
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......@@ -41,23 +41,53 @@ are stored efficiently without padding.
Building and Testing
______________________________
A stable version of the module can be downloaded using::
XXX
Current installation and testing are done with::
XXX
A stable version of the module can be downloaded using the download button on this `page <https://gitlab.com/PANNAdevs/panna>`_
Here are the commands for installation::
XXX
As a python module PANNA-TFR does not require installation but it relies on numpy library version => 1.15.0 and tensorflow version => 1.13.0
Test::
XXX
In order to set up and test the module, run the following::
$ tar -zxvf panna-master.tar.gz
$ cd panna-master
$ python3 ./panna/test-tfr-packer.py
Usage
______
PANNA-TFR main script requires a configuration file that specifies the parameter of the calculation
such as location of descriptor files or how many descriptors to be packed in a single record file.
A typical command for using this module is as follows::
$ export PYTHONPATH=/path/to/panna/directory/panna
$ python3 tfr_packer.py --config tfr_configuration.ini
A detailed tutorial about the contents of the configuration file can be found
`here <https://gitlab.com/PANNAdevs/panna/blob/master/doc/tutorial/README_tutorial_2_data_preparation.md>`_.
In this comprehensive tutorial, how use this module with other modules such as PANNA-GVECT and PANNA-TOOLS
is also demonstrated. Together, these modules cover all the steps necessary whlie going from raw data to descriptors that can be
used in machine learning workflow.
Source Code
___________
To be released soon.
PANNA-TFR source is currently hosted on `gitlab <https://gitlab.com/PANNAdevs/panna>`_.
Further Information
______________________
The PANNA-TFR module is developed with the contributions of R. Lot, Y. Shaidu, F. Pellegrini, E. Kucukbenli
References
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PANNA manuscript:
[1] R. Lot, Y. Shaidu, F. Pellegrini, E. Kucukbenli.
`arxiv:1907.03055 <https://arxiv.org/abs/1907.03055>`_. Submitted (2019).
[2] J. Behler and M. Parrinello, Generalized Neural-Network
Representation of High-Dimensional Potential-Energy
Surfaces, Phys. Rev. Lett. 98, 146401 (2007)
[3] Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg.
ANI-1: An extensible neural network potential with DFT accuracy
at force field computational cost. Chemical Science,(2017), DOI: 10.1039/C6SC05720A