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Commit 001d5d67 authored by Luigi's avatar Luigi
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Init tutorial

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In this tutorial we will use the ElemNet neural network architecture (https://github.com/NU-CUCIS/ElemNet) to predict the volume per atom of inorganic compounds, where the open quantum materials database (OQMD) is used as a resource (specifically, the data is taken from Ward et. al., npj Comput. Mater. 2, 16028 (2016)).
assets/nn_regression/Logo_MPG.png

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assets/nn_regression/Logo_NOMAD.png

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assets/nn_regression/activation_functions.png

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assets/nn_regression/biological_neuron_analogy.png

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assets/nn_regression/cs_classification_first_example.png

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assets/nn_regression/mlp_example.png

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assets/nn_regression/nomadCoE.png

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assets/nn_regression/perceptron_binary_classification.png

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assets/nn_regression/regression_first_example.png

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{
"authors": [
"Leitherer, Andreas",
"Sbailò, Luigi",
"Ghiringhelli, Luca M.
],
"email": "leitherer@fhi-berlin.mpg.de",
"title": "Hands-on tutorial: Regression using multilayer perceptrons",
"description": "In this tutorial we will use the ElemNet neural network architecture (https://github.com/NU-CUCIS/ElemNet) to predict the volume per atom of inorganic compounds, where the open quantum materials database (OQMD) is used as a resource (specifically, the data is taken from Ward et. al., npj Comput. Mater. 2, 16028 (2016)).",
"url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-tutorial-template",
"link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/{tutorial}.ipynb",
"link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/{tutorial}.ipynb",
"updated": "2020-04-09",
"flags":{
"featured": true,
"top_of_list": false
},
"labels": {
"application_keyword": [
"Neural networks / deep learning",
"Descriptors"
],
"application_section": [
"Materials property prediction"
],
"application_system": [
"Inorganic compounds taken from the OQMD database"
],
"category": [
"Tutorial"
],
"data_analytics_method": [
"Neural networks"
],
"platform": [
"jupyter"
]
}
}
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# Ignore everything in this directory
*
# Except this file
!.gitignore
\ No newline at end of file
setup.py 0 → 100644
import json
from setuptools import setup, find_packages
with open('metainfo.json') as file:
metainfo = json.load(file)
setup(
name='nn_regression',
version='1.0',
author=', '.join(metainfo['authors']),
author_email=metainfo['email'],
url=metainfo['url'],
description=metainfo['title'],
long_description=metainfo['description'],
packages=find_packages(),
install_requires=['tensorflow<2.0', 'keras', 'numpy', 'scipy', 'matplotlib', 'pandas', 'seaborn', 'pymatgen', 'sklearn'],
)
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