Commit 06f2e78a authored by Luigi Sbailo's avatar Luigi Sbailo
Browse files

Generate list of tutorials

parent eb404102
......@@ -2,36 +2,36 @@
"tutorials": [
{
"authors": [
"Daniel Speckhard",
"Andreas Leitherer",
"Luca Ghiringhelli"
"Leitherer, Andreas",
"Sbail\u00f2, Luigi",
"Ghiringhelli, Luca M."
],
"email": "speckhard@fhi-berlin.mpg.de",
"title": "Decision tree tutorial",
"description": "In this tutorial we will introduce decision trees. We go through a toy model introducing the SKLearn API. We then discuss piece by piece the different theoretical aspects of trees. We then move to training a regression tree and classification tree on different datasets related to materials sceience. We end the tutorial by covering random forests and bagging classfifers.",
"link": "https://nomad-lab.eu/prod/analytics/workshop/user-redirect/notebooks/tutorials/decision_tree.ipynb",
"link_video": "https://youtu.be/YBy9STVaqvU",
"updated": "2020-12-08",
"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",
"updated": "2020-04-09",
"flags": {
"featured": true,
"top_of_list": false,
"video": true
"top_of_list": false
},
"labels": {
"application_keyword": [
"Images"
"Neural networks / deep learning",
"Descriptors"
],
"application_section": [
"Tutorials"
"Materials property prediction"
],
"application_system": [
"Tutorial"
"Inorganic compounds taken from the OQMD database"
],
"category": [
"Tutorials"
"Tutorial"
],
"data_analytics_method": [
"Decison tree"
"Neural networks"
],
"platform": [
"jupyter"
......@@ -40,35 +40,43 @@
},
{
"authors": [
"Langer, Marcel F."
"Sbail\u00f2, Luigi",
"Ghiringhelli, Luca M."
],
"email": "langer@fhi-berlin.mpg.de",
"title": "Kernel Ridge Regression for Materials Property Prediction: A Tutorial Introduction",
"description": "In this tutorial, we'll explore the application of kernel ridge regression to the prediction of materials properties. We will begin with a largely informal, pragmatic introduction to kernel ridge regression, including a rudimentary implementation, in order to become familiar with the basic terminology and considerations. We will then discuss representations, and re-trace the NOMAD 2018 Kaggle challenge.",
"link": "https://nomad-lab.eu/prod/analytics/workshop/user-redirect/notebooks/tutorials/krr4mat.ipynb",
"link_video": "https://youtu.be/H_MVlljpYHw",
"updated": "2020-10-30",
"email": "sbailo@fhi-berlin.mpg.de",
"title": "Exploratory analysis of octet-binary compounds ",
"description": "Exploratory analyses make use of unsupervised learning techniques to extract information from unknown datasets. In this tutorial, we make use of some of the most popular clustering and dimension reduction algorithms to analyze a dataset composed of 82 octet-binary compounds.",
"url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-exploratory-analysis",
"link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/exploratory_analysis_tutorial.ipynb",
"updated": "2021-01-04",
"flags": {
"featured": true,
"top_of_list": false,
"video": true
"top_of_list": false
},
"labels": {
"application_keyword": [
"Formation energy prediction"
"k-means",
"Hierarchical clustering",
"DBSCAN",
"HDBSCAN",
"DenPeak",
"PCA",
"t-SNE",
"MDS",
"Octet binaries"
],
"application_section": [
"Tutorials"
"Tutorials for artificial-intelligence methods"
],
"application_system": [
"Group-III oxides"
"octet-binary materials"
],
"category": [
"Tutorials"
"Tutorial"
],
"data_analytics_method": [
"Kernel ridge regression",
"SOAP"
"Clustering",
"Dimension reduction"
],
"platform": [
"jupyter"
......@@ -77,43 +85,78 @@
},
{
"authors": [
"Ahmetcik, Emre",
"Ziletti, Angelo",
"Ouyang, Runhai",
"Luigi Sbailò",
"Leitherer, Andreas",
"Ghiringhelli, Luca M."
],
"email": "ghiringhelli@fhi-berlin.mpg.de",
"title": "Convolutional Neural network tutorial ",
"description": "In this tutorial, we briefly introduce the main ideas behind convolutional neural networks, build a neural network model with Keras, and explain the classification decision process using attentive response maps.",
"url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-convolutional-nn",
"link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/convolutional_nn.ipynb",
"updated": "2019-04-01",
"flags": {
"featured": true,
"top_of_list": false
},
"labels": {
"application_keyword": [
"Classification",
"Neural Networks"
],
"application_section": [
"Tutorials for artificial-intelligence methods"
],
"application_system": [
"Images"
],
"category": [
"Tutorial"
],
"data_analytics_method": [
"Convolutional Neural networks",
"Attentive response map"
],
"platform": [
"jupyter"
]
}
},
{
"authors": [
"Sbail\u00f2, Luigi",
"Scheffler, Matthias",
"Ghiringhelli, Luca M."
],
"email": "ghiringhelli@fhi-berlin.mpg.de",
"title": "Symbolic regression via compressed sensing: a tutorial",
"description": "In this tutorial we will show how to find descriptive parameters to predict materials properties using symbolic regrression combined with compressed sensing tools. The relative stability of the zincblende (ZB) versus rocksalt (RS) structure of binary materials is predicted and compared against a model trained with kernel ridge regression.",
"link": "https://nomad-lab.eu/prod/analytics/workshop/user-redirect/notebooks/tutorials/compressed_sensing.ipynb",
"link_video": "https://youtu.be/73mLp6C2opY",
"updated": "2020-12-8",
"title": "Querying the Archive and performing Artificial Intelligence modeling",
"description": "In this tutorial, we demonstrate how to query the NOMAD Archive from the NOMAD Analytics toolkit. We then show examples of machine learning analysis performed on the retrieved data set.",
"url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-query-nomad-archive",
"link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/query_nomad_archive.ipynb",
"link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/query_nomad_archive.ipynb",
"updated": "2020-05-03",
"flags": {
"featured": true,
"top_of_list": false,
"video": true
"top_of_list": false
},
"labels": {
"application_keyword": [
"Compressed sensing",
"Symbolic regression",
"Descriptors"
"Materials properties prediction",
"Data visualization"
],
"application_section": [
"Tutorials"
"Analysing the content of the Archive"
],
"application_system": [
"Octet binary materials"
"Materials"
],
"category": [
"Tutorial"
],
"data_analytics_method": [
"LASSO",
"SISSO",
"Kernel ridge regression"
"Clustering",
"Dimension reduction",
"Random forest"
],
"platform": [
"jupyter"
......
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