Electronic Stoping Power Neural Network predictor
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missing files in build
Project description
ESPNN - Electronic Stopping Power Neural Network
The ESPNN is a python-based deep neural network that allows the user to predict the electronic stopping power cross-section for any ion and target[^1] combination for a wide range of incident energies. The deep neural network was trained with many tens of thousands curated data points from the IAEA database. See more details of the ESPNN in this publication.
[^1]: ESPNN first release considers only mono-atomic targets.
Getting started
To use the ESPNN, we recommend using a python virtual environment. For example, anaconda or virtualenv. If you are not familiar with virtual environments and would like to rapidly start using python, follow the anaconda indications according to your operating system:
Before installing the package you may want to give it a try on Google Colab.
Install ESPNN
Using pip
The simplest way to install the ESPNN is via pip. Indistinctively, Ubuntu, Windows and macOS users can install the package by typing in the terminal or the anaconda bash terminal:
$ pip install ESPNN
Using this repository
You can also install the ESPNN package by cloning or downloading this repository. To clone (make sure you have git installed) this repo, use the following commands in your terminal/anaconda bash terminal:
$ git clone https://github.com/ale-mendez/ESPNN.git
$ cd ESPNN
$ pip install ESPNN/
If you downloaded the zip, change your directory to your download folder and, in your terminal/anaconda bash terminal, use
$ pip install ESPNN-master.zip
Run ESPNN in a notebook
A basic tutorial of the ESPNN package usage is given in prediction.ipynb. The package requires the following parameters as minimal input:
projectile
: Chemical formula for the projectileprojectile_mass
: Mass in amu for the projectiletarget
: Chemical formula for the targettarget_mass
: Mass in amu for the target
import ESPNN
ESPNN.run_NN(projectile='He', projectile_mass=4.002602, target='Au', target_mass=196.966569)
The package automatically produces a matplotlib
figure and a sample file named XY_prediction.dat
, where X
is the name of the projectile and Y
is the name of the target system.
$ ls -a
. .. HHe_prediction.dat prediction.ipynb
Optional arguments:
The energy grid used for the ESPNN calculation can be customized with arguments
emin
: Minimum energy value in MeV/amu units (default:0.001
)emax
: Maximum energy value in MeV/amu units (default:10
)npoints
: Number of grid points (default:1000
)
Furthermore, the figure plotting and output-file directory-path can be modified via
plot
: Prediction plot (default:True
)outdir
: Path to output folder (default:"./"
)
ESPNN.run_NN(projectile='He', projectile_mass=4.002602, target='Au', target_mass=196.966569, emin=0.01, emax=1, npoints=50)
Run ESPNN from terminal
The ESPNN package can also be used from terminal with a syntaxis analogous to the above given:
$ python -m ESPNN He 4.002602 Au 196.966569
Additional information about the optional arguments input can be obtained with the -h, --help flag:
$ python -m ESPNN -h
Funding Acknowledgements
The following institutions financially support this work: the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) by the PICT-2020-SERIEA-01931 and the Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT) of Argentina PIP-11220200102421CO. CCM also acknowledges the financial support of the IAEA.
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