From f172167a0b9f21e14b232627df37f1e2a6f1829a Mon Sep 17 00:00:00 2001
From: Adam <adam@fekete.co.uk>
Date: Mon, 1 Aug 2022 04:45:25 +0000
Subject: [PATCH] CI: Update metainfo in the GUI

---
 src/toolkitMetadata.json | 246 ++++++++++++++++++++++++++++++++++++++-
 1 file changed, 245 insertions(+), 1 deletion(-)

diff --git a/src/toolkitMetadata.json b/src/toolkitMetadata.json
index 3d49603..0a6729a 100644
--- a/src/toolkitMetadata.json
+++ b/src/toolkitMetadata.json
@@ -78,6 +78,44 @@
         ]
       }
     },
+    {
+      "authors": [
+        "Foppa, Lucas",
+        "Ghiringhelli, Luca M.",
+        "Scheffler, Matthias"
+      ],
+      "email": "foppa@fhi-berlin.mpg.de",
+      "title": "Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence",
+      "description": "This tutorial explores the application of SISSO to a consistent experimental data set in order to identify the key parameters correlated with the catalyst selectivity in propane oxidation.",
+      "notebook_name": "catalysis_MRS2021.ipynb",
+      "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/PropaneOxidation",
+      "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/catalysis_MRS2021.ipynb",
+      "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/catalysis_MRS2021.ipynb",
+      "link_paper": "https://link.springer.com/article/10.1557/s43577-021-00165-6",
+      "link_doi_paper": "https://link.springer.com/article/10.1557/s43577-021-00165-6",
+      "updated": "2022-6-23",
+      "flags": {
+        "featured": true,
+        "top_of_list": false
+      },
+      "labels": {
+        "application_section": [
+          "Timely artificial-intelligence applications to Materials Science"
+        ],
+        "application_system": [
+          "Heterogeneous catalysis"
+        ],
+        "category": [
+          "advanced_tutorial"
+        ],
+        "ai_methods": [
+          "SISSO"
+        ],
+        "platform": [
+          "jupyter"
+        ]
+      }
+    },
     {
       "authors": [
         "Sbail\u00f2, Luigi",
@@ -386,6 +424,44 @@
         ]
       }
     },
+    {
+      "authors": [
+        "Hassanzada, Qaem",
+        "Ghiringhelli, Luca M."
+      ],
+      "email": "ghiringhelli@fhi-berlin.mpg.de",
+      "title": "Introduction to dimension reduction of data",
+      "description": "In this tutorial...",
+      "notebook_name": "dimensionality_reduction.ipynb",
+      "url": "",
+      "link": "",
+      "link_public": "",
+      "updated": "2022-06-30",
+      "flags": {
+        "featured": true,
+        "top_of_list": false
+      },
+      "labels": {
+        "application_keyword": [
+          "DDR"
+        ],
+        "application_section": [
+          "Materials property prediction"
+        ],
+        "application_system": [
+          "Perovskite"
+        ],
+        "category": [
+          "beginner_tutorial"
+        ],
+        "ai_methods": [
+          "PCA-UMAP-MDS"
+        ],
+        "platform": [
+          "jupyter"
+        ]
+      }
+    },
     {
       "authors": [
         "Arif, Mohammad-Yasin",
@@ -698,6 +774,45 @@
         ]
       }
     },
+    {
+      "authors": [
+        "Purcell, Thomas A. R.",
+        "Scheffler, Matthias",
+        "Ghiringhelli, Luca M.",
+        "Carbogno, Christian"
+      ],
+      "email": "purcell@fhi-berlin.mpg.de",
+      "title": "Accelerated Materials Exploration via AI-Generated Maps",
+      "description": "Notebook recreating the results of the paper by the same title and authors.",
+      "notebook_name": "kappa_screening_sisso.ipynb",
+      "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-kappa_L_learning",
+      "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/kappa_screening_sisso.ipynb",
+      "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/kappa_screening_sisso.ipynb",
+      "updated": "2022-06-17",
+      "flags": {
+        "featured": true,
+        "top_of_list": false,
+        "paper": true
+      },
+      "labels": {
+        "application_section": [
+          "Thermal Conductivity"
+        ],
+        "application_system": [
+          "Solid State Crystals"
+        ],
+        "category": [
+          "thermal transport"
+        ],
+        "ai_methods": [
+          "SISSO",
+          "Sensitivy Analysis"
+        ],
+        "platform": [
+          "jupyter"
+        ]
+      }
+    },
     {
       "authors": [
         "Langer, Marcel F."
@@ -736,6 +851,47 @@
         ]
       }
     },
+    {
+      "authors": [
+        "Aakash Naik",
+        "Luigi Sbail\u00f2",
+        "Ahmetcik, Emre",
+        "Ziletti, Angelo",
+        "Ouyang, Runhai",
+        "Ghiringhelli, Luca",
+        "Scheffler, Matthias"
+      ],
+      "email": "ghiringhelli@fhi-berlin.mpg.de",
+      "title": "Predicting the metal-insulator classification of elements and binary systems",
+      "description": "This tutorial shows how to find descriptive parameters (short formulas) for the classification of materials properties. As an example, we address the classification of elemental and binary systems Ax\u200b\u200bBy\u200b\u200b into metals and non metals using experimental data extracted from the SpringerMaterials data base. The method is based on the algorithm sure independence screening and sparsifying operator (SISSO), which enables to search for optimal descriptors by scanning huge feature spaces. ",
+      "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-metalinsulator-prm2018",
+      "link": "",
+      "link_public": "",
+      "updated": "2021-12-1",
+      "flags": {
+        "featured": true,
+        "top_of_list": false
+      },
+      "labels": {
+        "application_section": [
+          "Timely artificial-intelligence applications to Materials Science"
+        ],
+        "application_system": [
+          "Binaries",
+          "Elements"
+        ],
+        "category": [
+          "advanced_tutorial"
+        ],
+        "ai_methods": [
+          "SISSO",
+          "Classification"
+        ],
+        "platform": [
+          "jupyter"
+        ]
+      }
+    },
     {
       "authors": [
         "Leitherer, Andreas",
@@ -796,7 +952,7 @@
       "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/perovskites_tolerance_factor.ipynb",
       "link_paper": "https://advances.sciencemag.org/content/advances/5/2/eaav0693.full.pdf",
       "link_doi_paper": "https://doi.org/10.1126/sciadv.aav0693",
-      "updated": "2021-12-14",
+      "updated": "2022-05-18",
       "flags": {
         "featured": true,
         "top_of_list": false,
@@ -827,6 +983,56 @@
         ]
       }
     },
+    {
+      "authors": [
+        "Oehlers, Milena",
+        "Sbailo,Luigi"
+      ],
+      "email": "milenaoehlers@gmail.com",
+      "title": "Proto- and Archetype Clustering-based SISSO",
+      "description": "In this tutorial two clustering methods, namely unsupervised k-means and supervised deep-aa, will be used to extract proto- and archetypes, respectively, along with corresponding clusters. The set of proto- or archetypes can be used as a substantially reduced training set for Single-Task SISSO, which outperforms random selection, while the corresponding clusters allow for an educated material2task-assignment of all training and test materials for Multi-Task SISSO, whose training on the whole training set outperforms corresponding training of Single-Task SISSO.",
+      "notebook_name": "proto_archetype_clustering_sisso.ipynb",
+      "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/proto_archetype_clustering_sisso",
+      "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/proto_archetype_clustering_sisso.ipynb",
+      "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/proto_archetype_clustering_sisso.ipynb",
+      "updated": "2021-12-20",
+      "flags": {
+        "featured": false,
+        "top_of_list": false
+      },
+      "labels": {
+        "application_keyword": [
+          "k-means",
+          "deep-aa",
+          "SISSO",
+          "sisso",
+          "archetypes",
+          "prototypes",
+          "clustering",
+          "training set reduction",
+          "multi-task",
+          "single-task",
+          "unsupervised",
+          "supervised"
+        ],
+        "application_section": [
+          "Tutorials for artificial-intelligence methods"
+        ],
+        "application_system": [
+          "System"
+        ],
+        "category": [
+          "beginner_tutorial"
+        ],
+        "ai_methods": [
+          "Clustering",
+          "SISSO"
+        ],
+        "platform": [
+          "jupyter"
+        ]
+      }
+    },
     {
       "authors": [
         "Sbail\u00f2, Luigi",
@@ -987,6 +1193,44 @@
         ]
       }
     },
+    {
+      "authors": [
+        "Hassanzada, Qaem",
+        "Ghiringhelli, Luca M."
+      ],
+      "email": "ghiringhelli@fhi-berlin.mpg.de",
+      "title": "An introduction to support-vector machine for classification",
+      "description": "In this tutorial...",
+      "notebook_name": "svm_classification.ipynb",
+      "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-svm_classification",
+      "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/svm_classification.ipynb",
+      "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/svm_classification.ipynb",
+      "updated": "2022-03-31",
+      "flags": {
+        "featured": true,
+        "top_of_list": false
+      },
+      "labels": {
+        "application_keyword": [
+          "SVM"
+        ],
+        "application_section": [
+          "Materials property prediction"
+        ],
+        "application_system": [
+          "Perovskite"
+        ],
+        "category": [
+          "beginner_tutorial"
+        ],
+        "ai_methods": [
+          "SVM"
+        ],
+        "platform": [
+          "jupyter"
+        ]
+      }
+    },
     {
       "authors": [
         "Regler, Benjamin",
-- 
GitLab