Commit b8f2adaf authored by Emine Kucukbenli's avatar Emine Kucukbenli

updated panna-charges

parent f334008a
Pipeline #4784 passed with stages
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......@@ -26,36 +26,65 @@ Purpose of Module
PANNA-Charges module demonstrates how to train a neural network to predict local atomic charges.
This network can later be used to calculat the electrostatic energy density of a crystal.
This network can later be used to calculate the electrostatic energy density of a crystal.
See Reference 2 for the theoretical model behind this approach.
PANNA-Charge, following other modules within the PANNA project, uses TensorFlow framework.
PANNA-Charges, following other modules within the PANNA project, uses TensorFlow framework.
PANNA-Charge supports periodic and aperiodic structures, multiple species,
and different network architecture for each species.
and a different all-to-all connected network architecture for each species.
It further supports controlling the training dynamics: eg. reeze/unfreeze layers, weight transfer, decaying learning rates etc.
Building and Testing
A stable version of the module can be downloaded using::
Current installation and testing are done with::
A stable version of the module can will be released in the near future,
and will be available for download using the download button on this `page <>`_
Here are the commands for installation::
As a python module PANNA-Charges does not require installation but it relies on numpy library version >= 1.15.0, tensorflow version >= 1.13.0, and
tensorboard version >= 1.13.0. Note that with version 2.0.0, tensorflow libraries went under substantial changes in structure, the 1.1X.X
family supports the equally valid previous structure and is still being maintained. PANNA-TRAIN requires tensorflow 1.1X.X family of versions.
In order to set up and test the module, run the following::
$ tar -zxvf panna-master.tar.gz
$ cd panna-master
$ python3 ./panna/
PANNA-Charges main script,, requires a configuration file that specifies the parameter of the calculation
such as number of layers and nodes of each neural network layer, learning parameter etc.
A typical command for using this module is as follows::
$ export PYTHONPATH=/path/to/panna/directory/panna
$ python3 --config charges_train_config.ini
A detailed tutorial about the contents of the configuration file will be released
`here <>`_.
In this comprehensive tutorial, a neural network training scenario for systems with long range interactions will be demonstrated.
Source Code
To be released soon.
PANNA-Charges source is not currently public, when it is released it will be hosted on `gitlab <>`_.
Further Information
The PANNA-Charges module is developed with the contributions of Y. Shaidu, R. Lot, F. Pellegrini, E. Kucukbenli
PANNA manuscript:
[1] R. Lot, Y. Shaidu, F. Pellegrini, E. Kucukbenli.
`arxiv:1907.03055 <>`_. Submitted (2019).
[2] N. Artrith, T. Morawietz, J. Behler. PRB 83, 153101 (2011).
High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide.
Erratum: PRB 86, 079914 (2012).
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