diff --git a/.gitignore b/.gitignore
index 25384ba2bbc456d31f721e91bb67cae9fd0a689a..13204b6f816513d708cfecfb7f4ebce19873f22a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -31,3 +31,4 @@ setup.json
parser.osio.log
gui/src/metainfo.json
gui/src/searchQuantities.json
+gui/src/parserMetadata.json
diff --git a/Dockerfile b/Dockerfile
index afe60f2b0a5ca73dc63a6b646ab231a10a150b47..33526c4467ad3eb52d475766d22157afbdea0676 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -69,6 +69,8 @@ RUN python setup.py sdist
RUN cp dist/nomad-lab-*.tar.gz dist/nomad-lab.tar.gz
RUN python -m nomad.cli dev metainfo > gui/src/metainfo.json
RUN python -m nomad.cli dev search-quantities > gui/src/searchQuantities.json
+RUN python -m nomad.cli dev parser-metadata > gui/src/parserMetadata.json
+RUN python -m nomad.cli dev toolkit-metadata > gui/src/toolkitMetadata.json
WORKDIR /install/docs
RUN make html
RUN \
@@ -87,6 +89,8 @@ RUN yarn
COPY gui /app
COPY --from=build /install/gui/src/metainfo.json /app/src/metainfo.json
COPY --from=build /install/gui/src/searchQuantities.json /app/src/searchQuantities.json
+COPY --from=build /install/gui/src/parserMetadata.json /app/src/parserMetadata.json
+COPY --from=build /install/gui/src/toolkitMetadata.json /app/src/toolkitMetadata.json
RUN yarn run build
# Build the Encyclopedia GUI in the gui build image
diff --git a/dependencies/parsers/abinit b/dependencies/parsers/abinit
index fe980c53e536f843b6e0ca85642892b8e2f75fed..342f391804c7fa7de28ddea5eb80ee9402758fab 160000
--- a/dependencies/parsers/abinit
+++ b/dependencies/parsers/abinit
@@ -1 +1 @@
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+Subproject commit 342f391804c7fa7de28ddea5eb80ee9402758fab
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index e7077c9a824e01b45b0e4989858ecd35f59f4cc7..f2cf15804d33a9f536407321ab768a8367426820 160000
--- a/dependencies/parsers/amber
+++ b/dependencies/parsers/amber
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+Subproject commit f2cf15804d33a9f536407321ab768a8367426820
diff --git a/dependencies/parsers/aptfim b/dependencies/parsers/aptfim
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+++ b/dependencies/parsers/aptfim
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+++ b/dependencies/parsers/band
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+++ b/dependencies/parsers/dl-poly
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--- a/dependencies/parsers/dmol3
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+Subproject commit 94dc1aa46ac7c5c0f1cbb6f1a00798331e0b5200
diff --git a/dependencies/parsers/tinker b/dependencies/parsers/tinker
index 7f5fc6a358dc7e40e716cc5205fa04e8cf9c6d6c..2775787b5b0c1a0d2f4bf21f5903cddb99aa81e0 160000
--- a/dependencies/parsers/tinker
+++ b/dependencies/parsers/tinker
@@ -1 +1 @@
-Subproject commit 7f5fc6a358dc7e40e716cc5205fa04e8cf9c6d6c
+Subproject commit 2775787b5b0c1a0d2f4bf21f5903cddb99aa81e0
diff --git a/dependencies/parsers/turbomole b/dependencies/parsers/turbomole
index 901bd3fcf50ce54e9b947c11476ee1a8371b3bae..6faaa3508f0db2d35f103cffd2c003459a93c82e 160000
--- a/dependencies/parsers/turbomole
+++ b/dependencies/parsers/turbomole
@@ -1 +1 @@
-Subproject commit 901bd3fcf50ce54e9b947c11476ee1a8371b3bae
+Subproject commit 6faaa3508f0db2d35f103cffd2c003459a93c82e
diff --git a/dependencies/parsers/vasp b/dependencies/parsers/vasp
index bd9c04e281aa42010d3e57310f1106680a332763..394b45037a8a4a120a9c492785aa8f1ff96d0d4d 160000
--- a/dependencies/parsers/vasp
+++ b/dependencies/parsers/vasp
@@ -1 +1 @@
-Subproject commit bd9c04e281aa42010d3e57310f1106680a332763
+Subproject commit 394b45037a8a4a120a9c492785aa8f1ff96d0d4d
diff --git a/dependencies/parsers/wien2k b/dependencies/parsers/wien2k
index d58ac9b61ba2c9e82f50a30c2c234778743d51ad..6007fb8dc88b225e42baaf9725867c9ed0caf257 160000
--- a/dependencies/parsers/wien2k
+++ b/dependencies/parsers/wien2k
@@ -1 +1 @@
-Subproject commit d58ac9b61ba2c9e82f50a30c2c234778743d51ad
+Subproject commit 6007fb8dc88b225e42baaf9725867c9ed0caf257
diff --git a/gui/src/components/About.js b/gui/src/components/About.js
index 8411177b5de46dbd3f167b251ee7631a4da7fb12..4af36318d1e251809a8497f3d906ede9acb65f9c 100644
--- a/gui/src/components/About.js
+++ b/gui/src/components/About.js
@@ -1,4 +1,4 @@
-import React, { useContext, useLayoutEffect, useRef, useCallback, useEffect } from 'react'
+import React, { useContext, useLayoutEffect, useRef, useCallback, useEffect, useState } from 'react'
import {ReactComponent as AboutSvg} from './about.svg'
import PropTypes from 'prop-types'
import Markdown from './Markdown'
@@ -6,28 +6,102 @@ import { appBase, optimadeBase, apiBase, debug, consent, aitoolkitEnabled, encyc
import { apiContext } from './api'
import packageJson from '../../package.json'
import { domains } from './domains'
-import { Grid, Card, CardContent, Typography, makeStyles, Link } from '@material-ui/core'
+import { Grid, Card, CardContent, Typography, makeStyles, Link, Dialog, DialogTitle, DialogContent, DialogActions, Button } from '@material-ui/core'
import { Link as RouterLink, useHistory } from 'react-router-dom'
-import tutorials from './aitoolkit/tutorials'
+import tutorials from '../toolkitMetadata'
+import parserMetadata from '../parserMetadata'
-export const CodeList = () => {
- const {info} = useContext(apiContext)
+function CodeInfo({code, ...props}) {
+ if (!code) {
+ return ''
+ }
+
+ const metadata = parserMetadata[code]
- if (!info) {
- return '...'
+ let introduction = `
+ For [${metadata.codeLabel || code}](${metadata.codeUrl}) please provide
+ all files that were used as input, were output by the code, or were produced you.
+ `
+ if (metadata.tableOfFiles && metadata.tableOfFiles !== '') {
+ introduction = `
+ For [${metadata.codeLabel || code}](${metadata.codeUrl}) please provide at least
+ the files from the following table (if applicable). Ideally, you upload
+ all files that were used as input, were output by the code, or were produced you.
+ `
}
- return info.codes.reduce((result, code, index) => {
- if (index !== 0) {
- result.push(', ')
+ return <Dialog open={true} {...props}>
+ <DialogTitle>{metadata.codeLabel || code}</DialogTitle>
+ <DialogContent>
+ <Markdown>{`
+ ${introduction} NOMAD will present all files in the same directory for each
+ recognized calculation. This works best, if you put all files that belong to
+ individual code runs into individual directories or only combine files from a few
+ runs in the same directory.
+
+ ${metadata.tableOfFiles}
+
+ ${(metadata.parserSpecific && metadata.parserSpecific !== '' &&
+ `Please note specifically for ${metadata.codeLabel || code}: ${metadata.parserSpecific}`) || ''}
+
+ To create an upload with all calculations in a directory structure:
+
+ \`\`\`
+ zip -r <upload-file>.zip <directory>/*
+ \`\`\`
+
+ You can find further information on [the project page for NOMAD's ${metadata.codeLabel || code} parser](${metadata.parserGitUrl}).
+ `}</Markdown>
+ </DialogContent>
+ <DialogActions>
+ <Button onClick={() => props.onClose && props.onClose()} color="primary">
+ close
+ </Button>
+ </DialogActions>
+ </Dialog>
+}
+CodeInfo.propTypes = {
+ code: PropTypes.string,
+ onClose: PropTypes.func
+}
+
+export const CodeList = ({withUploadInstructions}) => {
+ const [selected, setSelected] = useState(null)
+
+ const codes = Object.keys(parserMetadata).map(code => {
+ const metadata = parserMetadata[code]
+ if (!metadata) {
+ return code
+ }
+
+ if (withUploadInstructions) {
+ return <Link
+ href="#" key={code} onClick={() => setSelected(code)}
+ >{code.codeLabel || code}</Link>
}
- if (code.code_homepage) {
- result.push(<Link target="external" key={code.code_name} href={code.code_homepage}>{code.code_name}</Link>)
- } else {
- result.push(code.code_name)
+
+ if (metadata.codeUrl) {
+ return <Link href={metadata.codeUrl} key={code} target="code">{code.codeLabel || code}</Link>
}
- return result
+
+ return code
+ })
+
+ const toRender = codes.reduce((list, value, index) => {
+ if (index !== 0) {
+ list.push(', ')
+ }
+ list.push(value)
+ return list
}, [])
+
+ return <React.Fragment>
+ {toRender}
+ <CodeInfo code={selected} onClose={() => setSelected(null)} />
+ </React.Fragment>
+}
+CodeList.propTypes = {
+ withUploadInstructions: PropTypes.bool
}
const useCardStyles = makeStyles(theme => ({
diff --git a/gui/src/components/aitoolkit/AIToolkitPage.js b/gui/src/components/aitoolkit/AIToolkitPage.js
index 851b728d761c8e8e6355e1e7efcb70b07625d41a..62941a6681d16c5c26b90e11275d497a6e09e910 100644
--- a/gui/src/components/aitoolkit/AIToolkitPage.js
+++ b/gui/src/components/aitoolkit/AIToolkitPage.js
@@ -1,6 +1,6 @@
import React, { useMemo } from 'react'
import { Typography, ExpansionPanel, ExpansionPanelSummary, ExpansionPanelDetails, makeStyles, Link, ExpansionPanelActions, Button, Grid, TextField } from '@material-ui/core'
-import tutorials from './tutorials'
+import tutorials from '../../toolkitMetadata'
import ExpandMoreIcon from '@material-ui/icons/ExpandMore'
import Markdown from '../Markdown'
import { StringParam, useQueryParams, useQueryParam } from 'use-query-params'
diff --git a/gui/src/components/aitoolkit/tutorials.json b/gui/src/components/aitoolkit/tutorials.json
deleted file mode 100644
index 6bd8eb0f5364d26b033419eba71f9ada089804db..0000000000000000000000000000000000000000
--- a/gui/src/components/aitoolkit/tutorials.json
+++ /dev/null
@@ -1,428 +0,0 @@
-{
- "tutorials": [
- {
- "authors": [
- "Ahmetcik, Emre",
- "Ziletti, Angelo",
- "Ouyang, Runhai",
- "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.",
- "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-compressed-sensing",
- "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/compressed_sensing.ipynb",
- "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/compressed_sensing.ipynb",
- "updated": "2019-04-05",
- "flags": {
- "featured": true,
- "top_of_list": false
- },
- "labels": {
- "application_keyword": [
- "Compressed sensing",
- "Symbolic regression",
- "Descriptors"
- ],
- "application_section": [
- "Tutorials for artificial-intelligence methods"
- ],
- "application_system": [
- "Octet binary materials"
- ],
- "category": [
- "Tutorial"
- ],
- "data_analytics_method": [
- "LASSO",
- "SISSO",
- "Kernel ridge regression"
- ],
- "platform": [
- "jupyter"
- ]
- }
- },
- {
- "authors": [
- "Ziletti, Angelo",
- "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",
- "link_public": "https://analytics-toolkit.nomad-coe.eu/public/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": [
- "Ziletti, Angelo",
- "Ahmetcik, Emre",
- "Arif, Mohammad-Yasin",
- "Sbail\u00f2, Luigi",
- "Ouyang, Runhai",
- "Ghiringhelli, Luca M.",
- "Scheffler, Matthias"
- ],
- "email": "ghiringhelli@fhi-berlin.mpg.de",
- "title": "Predicting energy differences between crystal structures: (Meta-)stability of octet-binary compounds",
- "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.",
- "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-descriptor-role",
- "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/descriptor_role.ipynb",
- "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/descriptor_role.ipynb",
- "updated": "2020-06-14",
- "flags": {
- "featured": true,
- "top_of_list": false
- },
- "labels": {
- "application_keyword": [
- "Octet binaries"
- ],
- "application_section": [
- "Timely artificial-intelligence applications to Materials Science"
- ],
- "application_system": [
- "Rock salt",
- "Zinc blende"
- ],
- "category": [
- "Tutorial"
- ],
- "data_analytics_method": [
- "SISSO"
- ],
- "platform": [
- "jupyter"
- ]
- }
- },
- {
- "authors": [
- "Bieniek, Bj\u00f6rn",
- "Strange, Mikkel",
- "Carbogno, Christian",
- "Arif, Mohammad-Yasin",
- "Sbail\u00f2, Luigi",
- "Scheffler, Matthias"
- ],
- "email": "ghiringhelli@fhi-berlin.mpg.de",
- "title": "Error estimates from high-accuracy electronic structure reference calculations",
- "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 three different electronic-structure codes.",
- "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-error-estimates",
- "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/error_estimates.ipynb",
- "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/error_estimates.ipynb",
- "updated": "2020-07-03",
- "flags": {
- "featured": true,
- "top_of_list": false
- },
- "labels": {
- "application_keyword": [
- "Binaries",
- "Elemental solids"
- ],
- "application_section": [
- "Timely artificial-intelligence applications to Materials Science"
- ],
- "category": [
- "Demo"
- ],
- "data_analytics_method": [
- "Linear Least-squares Regression"
- ],
- "platform": [
- "jupyter"
- ]
- }
- },
- {
- "authors": [
- "Fekete, \u00c1d\u00e1m",
- "Stella, Martina",
- "Lambert, Henry",
- "De Vita, Alessandro",
- "Cs\u00e1nyi, G\u00e1bor"
- ],
- "email": "adam.fekete@kcl.ac.uk",
- "title": "The SOAP descriptor, Gaussian Approximation Potentials (GAP) and machine-learning of force fields",
- "description": "In this tutorial we will be using a Gaussian Approximation Potentials to analyse results of TB DFT calculations of Si surface. Along the way we will learn about different descriptors (2b, 3b, soap) to describe local atomic environment in order to predict energies and forces of Si surface.",
- "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-gap-si-surface",
- "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/gap_si_surface.ipynb",
- "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/gap_si_surface.ipynb",
- "updated": "2019-06-01",
- "flags": {
- "featured": true,
- "top_of_list": false
- },
- "labels": {
- "application_keyword": [
- "SOAP descriptor",
- "Gaussian Approximation Potentials (GAP)"
- ],
- "application_section": [
- "Tutorials for artificial-intelligence methods"
- ],
- "application_system": [
- "Si"
- ],
- "category": [
- "Tutorial"
- ],
- "data_analytics_method": [
- "Gaussian-process regression",
- "Kernel ridge regression"
- ],
- "platform": [
- "jupyter"
- ]
- }
- },
- {
- "authors": [
- "Fekete, \u00c1d\u00e1m",
- "Stella, Martina",
- "Lambert, Henry",
- "De Vita, Alessandro",
- "Cs\u00e1nyi, G\u00e1bor"
- ],
- "email": "adam.fekete@kcl.ac.uk",
- "title": "Grain boundaries of alpha-Fe tutorial",
- "description": "In this tutorial we will be using a machine learning method (clustering) to analyse results of Grain Boundary (GB) calculations of alpha-iron. Along the way we will learn about different methods to describe local atomic environment in order to calculate properties of GBs. We will use these properties to separate the different regions of the GB using clustering methods. Finally we will determine how the energy of the GB is changing according to the angle difference of the regions.",
- "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-grain-boundaries",
- "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/grain_boundaries.ipynb",
- "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/grain_boundaries.ipynb",
- "updated": "2019-06-01",
- "flags": {
- "featured": true,
- "top_of_list": false
- },
- "labels": {
- "application_keyword": [
- "Grain boundaries"
- ],
- "application_section": [
- "Timely artificial-intelligence applications to Materials Science"
- ],
- "application_system": [
- "Iron"
- ],
- "category": [
- "Tutorial"
- ],
- "data_analytics_method": [
- "Clustering",
- "K-means",
- "Gaussian mixture"
- ],
- "platform": [
- "jupyter"
- ]
- }
- },
- {
- "authors": [
- "Liu, Xiangyue",
- "Sutton, Christopher",
- "Yamamoto, Takenori",
- "Lysogorskiy, Yury",
- "Blumenthal, Lars",
- "Hammerschmidt, Thomas",
- "Golebiowski, Jacek",
- "Ziletti, Angelo",
- "Scheffler, Matthias",
- "Ghiringhelli, Luca M."
- ],
- "email": "ghiringhelli@fhi-berlin.mpg.de",
- "title": "NOMAD 2018 Kaggle research competition",
- "description": "In this tutorial, we will explore the best results of the NOMAD 2018 Kaggle research competition. The goal of this competition was to develop machine-learning models for the prediction of two target properties: the formation energy and the bandgap energy of transparent semiconducting oxides. The purpose of the modelling is to facilitate the discovery of new such materials and allow for advancements in (opto)electronic technologies",
- "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-kaggle-competition",
- "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/kaggle_competition.ipynb",
- "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/kaggle_competition.ipynb",
- "updated": "2020-02-06",
- "flags": {
- "featured": true,
- "top_of_list": false
- },
- "labels": {
- "application_keyword": [
- "Formation energy prediction",
- "Band gap energy prediction"
- ],
- "application_section": [
- "Timely artificial-intelligence applications to Materials Science"
- ],
- "application_system": [
- "Group-III oxidess"
- ],
- "category": [
- "Tutorial"
- ],
- "data_analytics_method": [
- "Kernel ridge regression",
- "Neural networks",
- "SOAP",
- "n-gram"
- ],
- "platform": [
- "jupyter"
- ]
- }
- },
- {
- "authors": [
- "Sbail\u00f2, Luigi",
- "Scheffler, Matthias",
- "Ghiringhelli, Luca M."
- ],
- "email": "ghiringhelli@fhi-berlin.mpg.de",
- "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-repository",
- "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
- },
- "labels": {
- "application_keyword": [
- "Materials properties prediction",
- "Data visualization"
- ],
- "application_section": [
- "Analysing the content of the Archive"
- ],
- "application_system": [
- "Materials"
- ],
- "category": [
- "Tutorial"
- ],
- "data_analytics_method": [
- "Clustering",
- "Dimension reduction",
- "Random forest"
- ],
- "platform": [
- "jupyter"
- ]
- }
- },
- {
- "authors": [
- "Cs\u00e1nyi, G\u00e1bor",
- "Kermode, James R."
- ],
- "email": "gc121@cam.ac.uk",
- "title": "Learning atomic charges",
- "description": "In this tutorial, we will use Gaussian process regression, GPR (or equivalently, Kernel Ridge Regression, KRR) to train and predict charges of atoms in small organic molecules.",
- "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-soap-atomic-charges",
- "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/soap_atomic_charges.ipynb",
- "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/soap_atomic_charges.ipynb",
- "updated": "2019-06-01",
- "flags": {
- "featured": true,
- "top_of_list": false
- },
- "labels": {
- "application_keyword": [
- "GDB molecular database"
- ],
- "application_section": [
- "Tutorials for artificial-intelligence methods"
- ],
- "application_system": [
- "GDB7"
- ],
- "category": [
- "Tutorial"
- ],
- "data_analytics_method": [
- "Gaussian-process regression",
- "Kernel ridge regression"
- ],
- "platform": [
- "jupyter"
- ]
- }
- },
- {
- "authors": [
- "Regler, Benjamin",
- "Scheffler, Matthias",
- "Ghiringhelli, Luca M."
- ],
- "email": "regler@fhi-berlin.mpg.de",
- "title": "Total cumulative mutual information",
- "description": "This interactive notebook includes the original implementation of total cumulative mutual information (TCMI) to reproduce the main results presented in the publication.",
- "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-tcmi",
- "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/tcmi.ipynb",
- "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/tcmi.ipynb",
- "updated": "2020-01-14",
- "flags": {
- "featured": true,
- "top_of_list": false
- },
- "labels": {
- "application_keyword": [
- "information theory",
- "mutual information",
- "cumulative entropy",
- "feature selection"
- ],
- "application_section": [
- "Timely artificial-intelligence applications to Materials Science"
- ],
- "application_system": [
- "System"
- ],
- "category": [
- "Tutorial"
- ],
- "data_analytics_method": [
- "Clustering",
- "TCMI"
- ],
- "language": [
- "python",
- "javascript"
- ],
- "platform": [
- "jupyter"
- ]
- }
- }
- ]
-}
\ No newline at end of file
diff --git a/gui/src/components/uploads/Upload.js b/gui/src/components/uploads/Upload.js
index f01ae2df04e8651ef7d3f8d37770bcbe7f05f9fe..271936fd4509acf25a1db04655813824747495aa 100644
--- a/gui/src/components/uploads/Upload.js
+++ b/gui/src/components/uploads/Upload.js
@@ -162,8 +162,7 @@ class Upload extends React.Component {
textAlign: 'right'
},
icon: {
- marginLeft: -theme.spacing(1) * 0.5,
- width: theme.spacing(13) - 2,
+ marginRight: theme.spacing(2),
alignItems: 'center',
display: 'flex'
},
diff --git a/gui/src/components/uploads/UploadPage.js b/gui/src/components/uploads/UploadPage.js
index fe15b8f35f16d2a8bf5b4b2714cf1582da9b2848..d878c693ee0be3706d8aba796cb17ae0d8919522 100644
--- a/gui/src/components/uploads/UploadPage.js
+++ b/gui/src/components/uploads/UploadPage.js
@@ -277,9 +277,8 @@ class UploadPage extends React.Component {
uploads</b> simultaneously. Your uploaded data is not published right away.
Find more details about uploading data in our <Link href={`${appBase}/docs/upload.html`}>documentation</Link> or visit
our <Link href="https://nomad-lab.eu/repository-archive-faqs">FAQs</Link>.
- </Typography>
- <Typography>
- The following codes are supported: <CodeList/>.
+ The following codes are supported: <CodeList withUploadInstructions />. Click
+ the code to get more specific information about how to prepare your files.
</Typography>
<Paper className={classes.dropzoneContainer}>
<Dropzone
diff --git a/gui/src/toolkitMetadata.json b/gui/src/toolkitMetadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..b25582e65c9e5afa52ca5ea8911d4c5a3e676044
--- /dev/null
+++ b/gui/src/toolkitMetadata.json
@@ -0,0 +1,426 @@
+{
+ "tutorials": [
+ {
+ "authors": [
+ "Ahmetcik, Emre",
+ "Ziletti, Angelo",
+ "Ouyang, Runhai",
+ "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.",
+ "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-compressed-sensing",
+ "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/compressed_sensing.ipynb",
+ "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/compressed_sensing.ipynb",
+ "updated": "2019-04-05",
+ "flags": {
+ "featured": true,
+ "top_of_list": false
+ },
+ "labels": {
+ "application_keyword": [
+ "Compressed sensing",
+ "Symbolic regression",
+ "Descriptors"
+ ],
+ "application_section": [
+ "Tutorials for artificial-intelligence methods"
+ ],
+ "application_system": [
+ "Octet binary materials"
+ ],
+ "category": [
+ "Tutorial"
+ ],
+ "data_analytics_method": [
+ "LASSO",
+ "SISSO",
+ "Kernel ridge regression"
+ ],
+ "platform": [
+ "jupyter"
+ ]
+ }
+ },
+ {
+ "authors": [
+ "Liu, Xiangyue",
+ "Sutton, Christopher",
+ "Yamamoto, Takenori",
+ "Lysogorskiy, Yury",
+ "Blumenthal, Lars",
+ "Hammerschmidt, Thomas",
+ "Golebiowski, Jacek",
+ "Ziletti, Angelo",
+ "Scheffler, Matthias",
+ "Ghiringhelli, Luca M."
+ ],
+ "email": "ghiringhelli@fhi-berlin.mpg.de",
+ "title": "NOMAD 2018 Kaggle research competition",
+ "description": "In this tutorial, we will explore the best results of the NOMAD 2018 Kaggle research competition. The goal of this competition was to develop machine-learning models for the prediction of two target properties: the formation energy and the bandgap energy of transparent semiconducting oxides. The purpose of the modelling is to facilitate the discovery of new such materials and allow for advancements in (opto)electronic technologies",
+ "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-kaggle-competition",
+ "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/kaggle_competition.ipynb",
+ "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/kaggle_competition.ipynb",
+ "updated": "2020-02-06",
+ "flags": {
+ "featured": true,
+ "top_of_list": false
+ },
+ "labels": {
+ "application_keyword": [
+ "Formation energy prediction",
+ "Band gap energy prediction"
+ ],
+ "application_section": [
+ "Timely artificial-intelligence applications to Materials Science"
+ ],
+ "application_system": [
+ "Group-III oxidess"
+ ],
+ "category": [
+ "Tutorial"
+ ],
+ "data_analytics_method": [
+ "Kernel ridge regression",
+ "Neural networks",
+ "SOAP",
+ "n-gram"
+ ],
+ "platform": [
+ "jupyter"
+ ]
+ }
+ },
+ {
+ "authors": [
+ "Ziletti, Angelo",
+ "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",
+ "link_public": "https://analytics-toolkit.nomad-coe.eu/public/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": [
+ "Fekete, \u00c1d\u00e1m",
+ "Stella, Martina",
+ "Lambert, Henry",
+ "De Vita, Alessandro",
+ "Cs\u00e1nyi, G\u00e1bor"
+ ],
+ "email": "adam.fekete@kcl.ac.uk",
+ "title": "The SOAP descriptor, Gaussian Approximation Potentials (GAP) and machine-learning of force fields",
+ "description": "In this tutorial we will be using a Gaussian Approximation Potentials to analyse results of TB DFT calculations of Si surface. Along the way we will learn about different descriptors (2b, 3b, soap) to describe local atomic environment in order to predict energies and forces of Si surface.",
+ "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-gap-si-surface",
+ "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/gap_si_surface.ipynb",
+ "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/gap_si_surface.ipynb",
+ "updated": "2019-06-01",
+ "flags": {
+ "featured": true,
+ "top_of_list": false
+ },
+ "labels": {
+ "application_keyword": [
+ "SOAP descriptor",
+ "Gaussian Approximation Potentials (GAP)"
+ ],
+ "application_section": [
+ "Tutorials for artificial-intelligence methods"
+ ],
+ "application_system": [
+ "Si"
+ ],
+ "category": [
+ "Tutorial"
+ ],
+ "data_analytics_method": [
+ "Gaussian-process regression",
+ "Kernel ridge regression"
+ ],
+ "platform": [
+ "jupyter"
+ ]
+ }
+ },
+ {
+ "authors": [
+ "Cs\u00e1nyi, G\u00e1bor",
+ "Kermode, James R."
+ ],
+ "email": "gc121@cam.ac.uk",
+ "title": "Learning atomic charges",
+ "description": "In this tutorial, we will use Gaussian process regression, GPR (or equivalently, Kernel Ridge Regression, KRR) to train and predict charges of atoms in small organic molecules.",
+ "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-soap-atomic-charges",
+ "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/soap_atomic_charges.ipynb",
+ "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/soap_atomic_charges.ipynb",
+ "updated": "2019-06-01",
+ "flags": {
+ "featured": true,
+ "top_of_list": false
+ },
+ "labels": {
+ "application_keyword": [
+ "GDB molecular database"
+ ],
+ "application_section": [
+ "Tutorials for artificial-intelligence methods"
+ ],
+ "application_system": [
+ "GDB7"
+ ],
+ "category": [
+ "Tutorial"
+ ],
+ "data_analytics_method": [
+ "Gaussian-process regression",
+ "Kernel ridge regression"
+ ],
+ "platform": [
+ "jupyter"
+ ]
+ }
+ },
+ {
+ "authors": [
+ "Fekete, \u00c1d\u00e1m",
+ "Stella, Martina",
+ "Lambert, Henry",
+ "De Vita, Alessandro",
+ "Cs\u00e1nyi, G\u00e1bor"
+ ],
+ "email": "adam.fekete@kcl.ac.uk",
+ "title": "Grain boundaries of alpha-Fe tutorial",
+ "description": "In this tutorial we will be using a machine learning method (clustering) to analyse results of Grain Boundary (GB) calculations of alpha-iron. Along the way we will learn about different methods to describe local atomic environment in order to calculate properties of GBs. We will use these properties to separate the different regions of the GB using clustering methods. Finally we will determine how the energy of the GB is changing according to the angle difference of the regions.",
+ "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-grain-boundaries",
+ "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/grain_boundaries.ipynb",
+ "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/grain_boundaries.ipynb",
+ "updated": "2019-06-01",
+ "flags": {
+ "featured": true,
+ "top_of_list": false
+ },
+ "labels": {
+ "application_keyword": [
+ "Grain boundaries"
+ ],
+ "application_section": [
+ "Timely artificial-intelligence applications to Materials Science"
+ ],
+ "application_system": [
+ "Iron"
+ ],
+ "category": [
+ "Tutorial"
+ ],
+ "data_analytics_method": [
+ "Clustering",
+ "K-means",
+ "Gaussian mixture"
+ ],
+ "platform": [
+ "jupyter"
+ ]
+ }
+ },
+ {
+ "authors": [
+ "Regler, Benjamin",
+ "Scheffler, Matthias",
+ "Ghiringhelli, Luca M."
+ ],
+ "email": "regler@fhi-berlin.mpg.de",
+ "title": "Total cumulative mutual information",
+ "description": "This interactive notebook includes the original implementation of total cumulative mutual information (TCMI) to reproduce the main results presented in the publication.",
+ "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-tcmi",
+ "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/tcmi.ipynb",
+ "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/tcmi.ipynb",
+ "updated": "2020-01-14",
+ "flags": {
+ "featured": true,
+ "top_of_list": false
+ },
+ "labels": {
+ "application_keyword": [
+ "information theory",
+ "mutual information",
+ "cumulative entropy",
+ "feature selection"
+ ],
+ "application_section": [
+ "Timely artificial-intelligence applications to Materials Science"
+ ],
+ "application_system": [
+ "System"
+ ],
+ "category": [
+ "Tutorial"
+ ],
+ "data_analytics_method": [
+ "Clustering",
+ "TCMI"
+ ],
+ "language": [
+ "python",
+ "javascript"
+ ],
+ "platform": [
+ "jupyter"
+ ]
+ }
+ },
+ {
+ "authors": [
+ "Arif, Mohammad-Yasin",
+ "Sbail\u00f2, Luigi",
+ "Purcell, Thomas A. R.",
+ "Ghiringhelli, Luca M.",
+ "Scheffler, Matthias"
+ ],
+ "email": "ghiringhelli@fhi-berlin.mpg.de",
+ "title": "Predicting energy differences between crystal structures: (Meta-)stability of octet-binary compounds",
+ "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.",
+ "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-descriptor-role",
+ "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/descriptor_role.ipynb",
+ "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/descriptor_role.ipynb",
+ "updated": "2020-09-5",
+ "flags": {
+ "featured": true,
+ "top_of_list": false
+ },
+ "labels": {
+ "application_keyword": [
+ "Octet binaries"
+ ],
+ "application_section": [
+ "Timely artificial-intelligence applications to Materials Science"
+ ],
+ "application_system": [
+ "Rock salt",
+ "Zinc blende"
+ ],
+ "category": [
+ "Tutorial"
+ ],
+ "data_analytics_method": [
+ "SISSO"
+ ],
+ "platform": [
+ "jupyter"
+ ]
+ }
+ },
+ {
+ "authors": [
+ "Bieniek, Bj\u00f6rn",
+ "Strange, Mikkel",
+ "Carbogno, Christian",
+ "Arif, Mohammad-Yasin",
+ "Sbail\u00f2, Luigi",
+ "Scheffler, Matthias"
+ ],
+ "email": "ghiringhelli@fhi-berlin.mpg.de",
+ "title": "Error estimates from high-accuracy electronic structure reference calculations",
+ "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 three different electronic-structure codes.",
+ "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-error-estimates",
+ "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/error_estimates.ipynb",
+ "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/error_estimates.ipynb",
+ "updated": "2020-07-03",
+ "flags": {
+ "featured": true,
+ "top_of_list": false
+ },
+ "labels": {
+ "application_keyword": [
+ "Binaries",
+ "Elemental solids"
+ ],
+ "application_section": [
+ "Timely artificial-intelligence applications to Materials Science"
+ ],
+ "category": [
+ "Demo"
+ ],
+ "data_analytics_method": [
+ "Linear Least-squares Regression"
+ ],
+ "platform": [
+ "jupyter"
+ ]
+ }
+ },
+ {
+ "authors": [
+ "Sbail\u00f2, Luigi",
+ "Scheffler, Matthias",
+ "Ghiringhelli, Luca M."
+ ],
+ "email": "ghiringhelli@fhi-berlin.mpg.de",
+ "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
+ },
+ "labels": {
+ "application_keyword": [
+ "Materials properties prediction",
+ "Data visualization"
+ ],
+ "application_section": [
+ "Analysing the content of the Archive"
+ ],
+ "application_system": [
+ "Materials"
+ ],
+ "category": [
+ "Tutorial"
+ ],
+ "data_analytics_method": [
+ "Clustering",
+ "Dimension reduction",
+ "Random forest"
+ ],
+ "platform": [
+ "jupyter"
+ ]
+ }
+ }
+ ]
+}
diff --git a/nomad/cli/dev.py b/nomad/cli/dev.py
index a63ee4380934cd614b11c70c51115469b7f080ff..ebc2f1125c114a22a08929bedb99deba742106df 100644
--- a/nomad/cli/dev.py
+++ b/nomad/cli/dev.py
@@ -162,3 +162,64 @@ def legacy_metainfo(path):
for element in path:
env = convert(element)
generate_metainfo_code(env)
+
+
+@dev.command(help='Generates a JSON file that compiles all the parser metadata from each parser project.')
+def parser_metadata():
+ import inspect
+ import re
+ import json
+ import yaml
+
+ from nomad.parsing import LegacyParser, FairdiParser
+ from nomad.parsing.parsers import parser_dict
+
+ parsers_metadata = {}
+ for parser in parser_dict.values():
+ if isinstance(parser, LegacyParser):
+ parser_class = parser.parser_class
+ elif isinstance(parser, FairdiParser):
+ parser_class = parser.__class__
+ else:
+ continue
+ parser_code_file = inspect.getfile(parser_class)
+
+ path_match = re.match(r'(.*/dependencies/parsers/[^/]+)/.*', parser_code_file)
+ if path_match:
+ parser_metadata_file = os.path.join(path_match.group(1), 'metadata.yaml')
+ if os.path.exists(parser_metadata_file):
+ with open(parser_metadata_file) as f:
+ parser_metadata = yaml.load(f, Loader=yaml.FullLoader)
+ parsers_metadata[parser.code_name] = parser_metadata
+
+ parsers_metadata = {
+ key: parsers_metadata[key]
+ for _, key in sorted([(key.lower(), key) for key in parsers_metadata], key=lambda x: x[0])}
+
+ print(json.dumps(parsers_metadata, indent=2))
+
+
+@dev.command(help='Generate toolkit tutorial metadata from anaytics submodules.')
+def toolkit_metadata():
+ import requests
+ import re
+ import json
+ modules = requests.get(
+ 'https://gitlab.mpcdf.mpg.de/api/v4/projects/3161/repository/files/.gitmodules/raw?ref=master').text
+
+ tutorials = []
+ lines = modules.split('\n')
+ for line in lines:
+ match = re.match(r'\s*url = (.*)$', line)
+ if match:
+ url = match.group(1).replace('.git', '') + '/-/raw/master/metainfo.json'
+ response = requests.get(url)
+ if response.status_code != 200:
+ print('Could not get metadata for %s' % match.group(1), file=sys.stderr)
+ continue
+ try:
+ tutorials.append(response.json())
+ except Exception:
+ print('Could not get metadata for %s. Project is probably not public.' % match.group(1), file=sys.stderr)
+
+ print(json.dumps(dict(tutorials=tutorials), indent=2))
diff --git a/nomad/config.py b/nomad/config.py
index c647b920af77ca2842073a3c4787525377163c48..8aaac11c6dac7fc0bc0c14d6e982d6a1b0bd3293 100644
--- a/nomad/config.py
+++ b/nomad/config.py
@@ -50,8 +50,8 @@ except ImportError:
from nomad import gitinfo
-warnings.filterwarnings("ignore", message="numpy.dtype size changed")
-warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
+warnings.filterwarnings('ignore', message='numpy.dtype size changed')
+warnings.filterwarnings('ignore', message='numpy.ufunc size changed')
class NomadConfig(dict):
diff --git a/nomad/parsing/__init__.py b/nomad/parsing/__init__.py
index 9ae227c6a0aec6331ad528fa438b658880c00fa3..2986e6f05e9c8717e0c5ff8023f03a1678aa0dd9 100644
--- a/nomad/parsing/__init__.py
+++ b/nomad/parsing/__init__.py
@@ -69,5 +69,5 @@ based on nomad@fairdi's metainfo:
'''
from nomad.parsing.legacy import Backend, BackendError, LegacyParser
-from nomad.parsing.parser import Parser, BrokenParser, MissingParser, MatchingParser
+from nomad.parsing.parser import Parser, BrokenParser, MissingParser, MatchingParser, FairdiParser
from nomad.parsing.artificial import TemplateParser, GenerateRandomParser, ChaosParser, EmptyParser