From 805a120ea3c932eb76c60c43d293571ff86b4c25 Mon Sep 17 00:00:00 2001
From: Luigi Sbailo <sbailo@fhi-berlin.mpg.de>
Date: Wed, 16 Dec 2020 23:25:31 +0100
Subject: [PATCH] Update clustering tutorial for production
---
tutorials.json | 122 +-----------------------
tutorials/analytics-clustering-tutorial | 2 +-
2 files changed, 4 insertions(+), 120 deletions(-)
diff --git a/tutorials.json b/tutorials.json
index 5a9f05ab..f72f5150 100644
--- a/tutorials.json
+++ b/tutorials.json
@@ -85,45 +85,6 @@
]
}
},
- {
- "authors": [
- "Leitherer, Andreas",
- "Sbail\u00f2, 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"
- ]
- }
- },
{
"authors": [
"Bieniek, Bj\u00f6rn",
@@ -207,8 +168,8 @@
"Ghiringhelli, Luca M."
],
"email": "sbailo@fhi-berlin.mpg.de",
- "title": "Introduction into clustering",
- "description": "In this tutorial we introduce into the most popular clustering algorithms",
+ "title": "Introduction to clustering",
+ "description": "In this tutorial we introduce to the most popular clustering algorithms. We focus on partitioning, hierarchical and density-based clustering algorithms, and methods are tested on artificial datasets of increasing complexity",
"url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-clustering-tutorial",
"link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/clustering_tutorial.ipynb",
"link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/clustering_tutorial.ipynb",
@@ -266,7 +227,7 @@
"Tutorials for artificial-intelligence methods"
],
"application_system": [
- "Images"
+ "Imates"
],
"category": [
"Tutorial"
@@ -364,46 +325,6 @@
]
}
},
- {
- "authors": [
- "Lucas Foppa",
- "Thomas Purcell",
- "Sbail\u00f2, Luigi",
- "Christopher Bartel",
- "Ghiringhelli, Luca M."
- ],
- "email": "ghiringhelli@fhi-berlin.mpg.de",
- "title": "Finding a tolerance factor to predict perovskite stability with SISSO",
- "description": "In this tutorial...",
- "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-perovskite-tolerance-factor",
- "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": [
- "SISSO"
- ],
- "application_section": [
- "Materials property prediction"
- ],
- "application_system": [
- "Perovskite"
- ],
- "category": [
- "Tutorial"
- ],
- "data_analytics_method": [
- "SISSO"
- ],
- "platform": [
- "jupyter"
- ]
- }
- },
{
"authors": [
"Langer, Marcel F."
@@ -524,43 +445,6 @@
]
}
},
- {
- "authors": [
- "Sbail\u00f2, Luigi",
- "Ghiringhelli, Luca M."
- ],
- "email": "ghiringhelli@fhi-berlin.mpg.de",
- "title": "...",
- "description": "...",
- "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-hisisso-perovskites",
- "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/hisisso_perovskites.ipynb",
- "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/hisisso_perovskites.ipynb",
- "updated": "2020-09-5",
- "flags": {
- "featured": true,
- "top_of_list": false
- },
- "labels": {
- "application_keyword": [
- "..."
- ],
- "application_section": [
- "..."
- ],
- "application_system": [
- "..."
- ],
- "category": [
- "..."
- ],
- "data_analytics_method": [
- "..."
- ],
- "platform": [
- "jupyter"
- ]
- }
- },
{
"authors": [
"Liu, Xiangyue",
diff --git a/tutorials/analytics-clustering-tutorial b/tutorials/analytics-clustering-tutorial
index 9a2365d7..1b386428 160000
--- a/tutorials/analytics-clustering-tutorial
+++ b/tutorials/analytics-clustering-tutorial
@@ -1 +1 @@
-Subproject commit 9a2365d74049bb80514e5c82bc3507035d6e779d
+Subproject commit 1b3864285d6a7d6d0e04754692622ca6f83fffbe
--
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