adding demos information

cluster-x/clusterX-x1.demoinfo.yaml

+{
+"id": "59fb07f59784de0031020891",
+"type": "demos",
+"attributes": {
+"title": "Predicting ground-states of binary alloys through cluster expansion: Example of a AlNa binary surface alloy",
+"logicalPath": "/data/shared/rsant/clusterX-x1.bkr",
+"authors": ["Santiago Rigamonti", "Maria Troppenz",  "Claudia Draxl"],
+"editLink": "/notebook-edit/data/shared/rsant/clusterX-x1.bkr",
+"isPublic": true,
+"username": "rsant",
+"description": "A tool for predicting ground state configurations of binary alloys. In this tutorial, the ground state configurations of a AlNa surface alloy are found. Starting from a set of ab initio data for random configurations of the alloy, a cluster expansion is performed and the ground states are found through a configurational sampling.",
+"created_at": "2017-10-06T16:00:10.746Z",
+"updated_at": "2017-10-06T16:00:10.746Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python"],
+	"data_analytics_method" : ["Cluster expansion"],
+	"application_section" : ["Predicting ground-states of alloys (convex hull construction)"],
+	"application_keywords": ["Binary alloys", "Surface", "AlNa"]
+}
+}
+}

cluster-x/clusterX-x2.demoinfo.yaml

+{
+"id": "59fb07f59784de003102087a",
+"type": "demos",
+"attributes": {
+"title": "Predicting ground-states of binary alloys through cluster expansion: Example of a SiGe binary alloy",
+"logicalPath": "/data/shared/rsant/clusterX-x2.bkr",
+"authors": ["Santiago Rigamonti", "Maria Troppenz",  "Claudia Draxl"],
+"editLink": "/notebook-edit/data/shared/rsant/clusterX-x2.bkr",
+"isPublic": true,
+"username": "rsant",
+"description": "A tool for predicting ground state configurations of binary alloys. In this tutorial, the ground state configurations of a SiGe binary alloy are found. Starting from a set of ab initio data for random configurations of the alloy, a cluster expansion is performed and the ground states are found through a configurational sampling.",
+"created_at": "2017-10-06T16:00:48.038Z",
+"updated_at": "2017-10-06T16:00:48.038Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python"],
+	"data_analytics_method" : ["Cluster expansion"],
+	"application_section" : ["Predicting ground-states of alloys (convex hull construction)"],
+	"application_keyword": ["Binary alloys", "Surface", "SiGe"]
+}
+}
+}

demos/collect.py

+import json, yaml, glob, logging, sys
+
+data = []
+for f in glob.glob("../*/*.demoinfo.yaml"):
+    try:
+        with open(f) as fIn:
+            d=yaml.load(fIn.read().replace('\t','        '))
+        idStr = d.get('id',f[2:])
+        typeStr = d.get('type',"demos")
+        attribs = d.get('attributes', {})
+        for k, v in d.items():
+            if k not in ['attributes', 'id', 'type']:
+                attribs[k] = v
+        data.append({
+            'id': idStr,
+            'type': 'demos',
+            'attributes': attribs
+        })
+    except:
+        logging.exception("failure for file %s",f)
+json.dump({'data':sorted(data)}, sys.stdout, indent = 2, sort_keys=True)

demos/creedo.demoinfo.yaml

+{
+"id": "creedoMain",
+"type": "demos",
+"attributes": {
+"title": "Discovering simple descriptors for crystal-structure classification",
+"logicalPath": "/Creedo/index.htm",
+"authors": ["Mario Boley", "Bryan Goldsmith", "Ankit Kariryaa", "Luca Ghiringhelli"],
+"editLink": "/Creedo/index.htm",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "In this tutorial, Subgroup Discovery (SGD) is used to identify simple descriptors for predicting whether an octet binary material crystallizes in rocksalt or zincblende crystal structures. SGD is a data-mining technique that is used to identify and describe local patterns (subgroups) in complex data. SGD will mine the data for subgroups that optimize the utility functions and at the same time cover (contain) as many materials as possible. The groups are described by combining one or more Boolean statements on the features (e.g., “the electron affinity of atom A (the cation) is smaller than 1.00”).",
+"created_at": "",
+"updated_at": "",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["creedo"],
+	"data_analytics_method" : ["Subgroup Discovery"],
+	"application_section" : ["Crystal structure prediction"],
+	"application_keyword": ["Octet Binaries", "Rock salt", "Zinc blende", "Gold clusters", "Structure property relationship"]
+}
+}
+}

embedding/Embedding.demoinfo.yaml

+{
+"id": "5a09a48180996e0031366343",
+"type": "demos",
+"attributes": {
+"title": "Embedding",
+"logicalPath": "/data/shared/tutorialsNew/embedding/Embedding.bkr",
+"authors": ["Angelo Ziletti", "Ankit Kariryaa", "Fawzi Mohamed", "Luca Ghiringhelli", "Matthias Scheffler"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/embedding/Embedding.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "A tool that produces two-dimensional structure maps for octet binary compounds, starting from a high-dimensional set of coordinates (features) that identify each material (data point), based on free-atom data of the atomic species constituting the binary material.",
+"created_at": "2017-11-13T13:56:41.082Z",
+"updated_at": "2017-11-13T13:56:41.082Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python", "javascript"],
+	"data_analytics_method" : ["Dimensionality Reduction", "Principal Component Analysis", "PCA", "Multidimensional scaling", "MDS", "t-Distributed Stochastic Neighbor Embedding" ,"t-SNE"],
+	"application_section" : ["Crystal structure prediction"],
+        "application_keyword": ["Octet Binaries", "Rock salt", "Zinc blende"],
+	"visualization" : ["NOMAD viewer"]
+}
+}
+}
+

errorbars/errorbars_html.demoinfo.yaml

+{
+"id": "5a09a48280996e0031366345",
+"type": "demos",
+"attributes": {
+"title": "Analyzing and estimating error bars from high-accuracy references",
+"logicalPath": "/data/shared/tutorialsNew/errorbars/errorbars_html.bkr",
+"authors": ["Björn Bieniek", "Mikkel Strange", "Christian Carbogno"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/errorbars/errorbars_html.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "A set of tools to analyze the error in electronic structure calculations due to the choice of numerical settings. We use the NOMAD infrastructure to systematically investigate the deviances in total and relative energies as function of typical settings for basis sets, k-grids, etc. for 71 elemental and 81 binary solids in four different electronic-structure codes.",
+"created_at": "2017-11-13T13:56:39.770Z",
+"updated_at": "2017-11-13T13:56:39.770Z",
+"labels" : { 
+	"category" : "Demo",
+	"platform" :  "beaker",
+	"language" :  ["python", "javascript"],
+	"data_analytics_method" : ["Linear Least-squares Regression"],
+	"application_section" : ["Error bars"],
+	"application_keyword": ["Binaries", "Elemental solids"]
+}
+}
+}
+

ff-fit/FF-fit.demoinfo.yaml

+{
+"id": "5a09a48280996e0031366347",
+"type": "demos",
+"attributes": {
+"title": "Complexity estimator for accurate-forces learning",
+"logicalPath": "/data/shared/tutorialsNew/ff-fit/FF-fit.bkr",
+"authors": ["Ádám Fekete", "Aldo Glielmo", "Martina Stella", "Alessandro De Vita"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/ff-fit/FF-fit.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "This tutorial makes use of Gaussian process (GP) regression in order to assess the complexity of a given system. This can be defined as the data set size necessary to the GP to predict a target property (eg. atomic forces, total energy of a configuration). The currently available data is the atomic forces in the mono-crystalline silicon at 300K, 1200K and 3000K.",
+"created_at": "2017-11-13T13:56:37.859Z",
+"updated_at": "2017-11-13T13:56:37.859Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python", "javascript"],
+	"data_analytics_method" : ["Gaussian process"],
+	"application_keyword" : ["monocrystalline Si", "Si"],
+	"reference" : ["https://arxiv.org/abs/1611.03877"]
+}
+}
+}

hands-on/hands-on-tutorial.demoinfo.yaml

+{
+"id": "5a09a48380996e0031366349",
+"type": "demos",
+"attributes": {
+"title": "Tutorial on compressed sensing for materials property prediction",
+"logicalPath": "/data/shared/tutorialsNew/hands-on/hands-on-tutorial.bkr",
+"authors": ["Emre Ahmetcik", "Angelo Ziletti", "Runhai Ouyang", "Luca Ghiringhelli", "Matthias Scheffler"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/hands-on/hands-on-tutorial.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "This tutorial introduces from scratch and step by step: Compressed sensing, LASSO, and SISSO for materials property prediction",
+"created_at": "2017-11-13T13:56:35.532Z",
+"updated_at": "2017-11-13T13:56:35.532Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python", "javascript"],
+	"data_analytics_method" : ["Compressed Sensing", "LASSO+l0", "SISSO"],
+	"application_section" : ["Crystal structure prediction"],
+	"application_keyword": ["Octet Binaries", "Rock salt", "Zinc blende", "CsCl", "CrB", "NiAs"],
+	"visualization" : ["NOMAD viewer"],
+	"reference" : ["https://doi.org/10.1103/PhysRevLett.114.105503","https://doi.org/10.1088/1367-2630/aa57bf","https://arxiv.org/abs/1710.03319"]
+}
+}
+}

lasso/LASSO_L0.demoinfo.yaml

+{
+"id": "5a09a48380996e0031366348",
+"type": "demos",
+"attributes": {
+"title": "Predicting energy differences between crystal structures. Smaller feature spaces.",
+"logicalPath": "/data/shared/tutorialsNew/lasso/LASSO_L0.bkr",
+"authors": ["Angelo Ziletti", "Emre Ahmetcik", "Runhai Ouyang", "Ankit Kariryaa", "Fawzi Mohamed", "Luca Ghiringhelli", "Matthias Scheffler"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/lasso/LASSO_L0.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "A tool for predicting the difference in the total energy between different polymorphs for 82 octet binary compounds, which gives an indication of the stability of the material. This is accomplished by identifying a set of descriptive parameters (a descriptor) from the free-atom data for the binary atomic species comprising the material using LASSO + l0-norm minimization approach.",
+"created_at": "2017-11-13T13:56:35.502Z",
+"updated_at": "2017-11-13T13:56:35.502Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python", "javascript"],
+	"data_analytics_method" : ["Compressed Sensing", "LASSO+l0"],
+	"application_section" : ["Crystal structure prediction"],
+        "application_keyword": ["Octet Binaries", "Rock salt", "Zinc blende"],
+	"visualization" : ["NOMAD viewer"],
+	"reference" : ["https://doi.org/10.1103/PhysRevLett.114.105503","https://doi.org/10.1088/1367-2630/aa57bf"]
+}
+}
+}

nomad-query/nomad-query.demoinfo.yaml

+{
+"id": "5a1bf3b780996e0031366356",
+"type": "demos",
+"attributes": {
+"title": "Querying and vizualising the content of the NOMAD Archive",
+"logicalPath": "/data/shared/tutorialsNew/nomad-query/nomad-query.bkr",
+"authors": ["Benjamin Regler", "Alfonso Sastre", "Fawzi Mohamed", "Luca Ghiringhelli"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/nomad-query/nomad-query.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "A tutorial introduction on how to perform a query over the NOMAD Archive, by means of a light and intuitive GUI, and browse the results",
+"created_at": "2017-11-27T11:15:03.028Z",
+"updated_at": "2017-11-27T11:15:03.028Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python", "javascript"],
+	"data_analytics_method" : ["ElasticSearch"],
+	"application_section" : ["Archive Query"]
+}
+}
+}

periodic-table/periodic-table.demoinfo.yaml

+{
+"id": "5a09a48480996e003136634a",
+"type": "demos",
+"attributes": {
+"title": "A periodic table of elements for atomic data collections",
+"logicalPath": "/data/shared/tutorialsNew/periodic-table/periodic-table.bkr",
+"authors": ["Benjamin Regler", "Luca Ghiringhelli", "Matthias Scheffler"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/periodic-table/periodic-table.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "In this notebook, we use the Nomad infrastructure to query a limited number of atomic data collections and to visualize them in the periodic table of elements. There are several filters that can be applied to atomic data collections and a field to select which atomic property should be visualized in the periodic table of elements.",
+"created_at": "2017-11-13T13:56:28.881Z",
+"updated_at": "2017-11-13T13:56:28.881Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python", "javascript"],
+	"application_keyword" : ["atomic species"]
+}
+}
+}
+
+

phase-diagram/phasediagram.demoinfo.yaml

+{
+"id": "5a09a48480996e003136634c",
+"type": "demos",
+"attributes": {
+"title": "Assessing the crystal-structure stability for a material under different synthetic conditions",
+"path": "/nomad/nomadlab/beaker-notebooks/user-data/shared/tutorialsNew/phase-diagram/phasediagram.bkr",
+"logicalPath": "/data/shared/tutorialsNew/phase-diagram/phasediagram.bkr",
+"authors": ["Mikkel Strange", "Kristian S. Thygesen"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/phase-diagram/phasediagram.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "Developed by .A tool that produces compositional phase diagrams. As an example we consider materials made of Li-Fe-P-O. Phase diagrams are generally useful to determine if a given material is thermodynamically stable under certain conditions such as temperature, pressure etc. In this tutorial, we consider only a compositional phase diagram at zero temperature and pressure.",
+"created_at": "2017-11-13T13:56:28.853Z",
+"updated_at": "2017-11-13T13:56:28.853Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python", "javascript"],
+	"application_section" : ["Predicting ground-states of alloys (convex hull construction)"],
+        "application_keyword": ["LiFePO"],
+	"reference" : ["https://doi.org/10.1021/cm702327g"]
+}
+}
+}
+
+

prototype/brprototype3.demoinfo.yaml

+{
+"id": "5a09a48480996e003136634b",
+"type": "demos",
+"attributes": {
+"title": "Predictions of ab initio properties of molecules and clusters",
+"logicalPath": "/data/shared/tutorialsNew/prototype/brprototype3.bkr",
+"authors": ["Matthias Rupp"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/prototype/brprototype3.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "A tool for building machine-learning models for rapidly and accurately predicting outcomes of electronic structure calculations (reference data). In this tutorial, kernel ridge regression models are built to predict the atomization energy of small organic molecules.",
+"created_at": "2017-11-13T13:56:28.774Z",
+"updated_at": "2017-11-13T13:56:28.774Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python", "javascript"],
+	"data_analytics_method" : ["Kernel ridge regression", "KRR"],
+	"application_section" : ["Organic molecules"],
+	"application_keyword": ["GDB7-12", "GDB7-13", "GDB9-14"],
+	"reference" : ["https://doi.org/10.1002/qua.24954"]
+}
+}
+}

sis/sis_cscl.demoinfo.yaml

+{
+"id": "5a09a48480996e003136634e",
+"type": "demos",
+"attributes": {
+"title": "Predicting energy differences between crystal structures",
+"logicalPath": "/data/shared/tutorialsNew/sis/sis_cscl.bkr",
+"authors": ["Angelo Ziletti", "Emre Ahmetcik", "Runhai Ouyang", "Ankit Kariryaa", "Fawzi Mohamed", "Luca Ghiringhelli", "Matthias Scheffler"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/sis/sis_cscl.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "A tool for predicting the difference in the total energy between different polymorphs for 82 octet binary compounds, which gives an indication of the stability of the material. This is accomplished by identifying a set of descriptive parameters (a descriptor) from the free-atom data for the binary atomic species comprising the material using the Sure Independent Screening (SIS) + l0-norm minimization approach.",
+"created_at": "2017-11-13T13:56:28.391Z",
+"updated_at": "2017-11-13T13:56:28.391Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python", "javascript"],
+	"data_analytics_method" : ["Compressed Sensing", "SISSO"],
+	"application_section" : ["Crystal structure prediction"],
+	"application_keyword": ["Octet Binaries", "Rock salt", "Zinc blende", "CsCl", "CrB", "NiAs"],
+	"visualization" : ["NOMAD viewer"],
+	"reference" : ["https://arxiv.org/abs/1710.03319"]
+}
+}
+}

sisso/sisso-metal-nonmetal.demoinfo.yaml

+{
+"id": "5a09a48480996e0031366351",
+"type": "demos",
+"attributes": {
+"title": "Predicting the metal-insulator classification of elements and binary systems",
+"logicalPath": "/data/shared/tutorialsNew/sisso/sisso-metal-nonmetal.bkr",
+"authors": ["Emre Ahmetcik", "Angelo Ziletti", "Runhai Ouyang", "Luca Ghiringhelli", "Matthias Scheffler"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/sisso/sisso-metal-nonmetal.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"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​​By​​ 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. ",
+"created_at": "2017-11-13T13:56:28.375Z",
+"updated_at": "2017-11-13T13:56:28.375Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python", "javascript"],
+	"data_analytics_method" : ["Compressed Sensing", "SISSO"],
+	"application_section" : ["Materials property prediction"],
+	"application_keyword": ["Binaries", "Metal/insulator", "Classification"],
+	"visualization" : ["NOMAD viewer"],
+	"reference" : ["https://arxiv.org/abs/1710.03319"]
+}
+}
+}
+

soap-similiarity/SOAP_similarity.demoinfo.yaml

+{
+"id": "5a09a48480996e003136634f",
+"type": "demos",
+"attributes": {
+"title": "Evaluating the (dis)similarity of crystalline, disordered, and molecular compounds",
+"logicalPath": "/data/shared/tutorialsNew/soap-similiarity/SOAP_similarity.bkr",
+"authors": ["Carl Poelking", "Angelo Ziletti",  "Luca Ghiringhelli",  "Gábor Csányi"],
+"editLink": "/notebook-edit/data/shared/tutorialsNew/soap-similiarity/SOAP_similarity.bkr",
+"isPublic": true,
+"username": "tutorialsNew",
+"description": "A tool for mapping and visualizing materials databases using generic kernel- and graph-based similarity measures together with the powerful Smooth Overlap of Atomic Positions (SOAP) descriptor for atomic environments.",
+"created_at": "2017-11-13T13:56:28.355Z",
+"updated_at": "2017-11-13T13:56:28.355Z",
+"labels" : { 
+	"category" : ["Demo"],
+	"platform" :  ["beaker"],
+	"language" :  ["python", "javascript"],
+	"data_analytics_method" : ["SOAP", "global similarity", "glosim", "kernel principal-component analysis", "kernel PCA", "Multidimensional scaling", "MDS"],
+	"application_section" : ["Crystal structure prediction"],
+	"application_keyword":  ["Octet Binaries", "Rock salt", "Zinc blende", "GDB7"],
+	"visualization" : ["NOMAD viewer"],
+	"reference" : ["https://doi.org/10.1039/C6CP00415F","https://arxiv.org/abs/1601.04077"]
+}
+}
+}