- {section.title}
-
+
+ 
+
+
+
+
+
+
+ 
+
+
+
+ By clicking on the 'Get to work' button you will access a
+ personal space that is available to each NOMAD user. After
+ logging in, you will see a 'tutorials' and 'work' directory.
+ The 'tutorials' directory contains all notebooks available in
+ the AI toolkit, while the 'work' directory offers some space
+ to save personal work. When you are in the 'work' directory,
+ click on the 'new' icon on the top right and then select
+ 'Python 3'. This will create a Jupyter notebook that is
+ stored in the AI toolkit and can be reaccessed and iteratively
+ modified by the user. All packages and software installed in the AI
+ toolkit are also available in the 'work' directory, that
+ makes it possible to employ the same code syntax used in
+ each tutorial contained in the AI toolkit for your own project.
+ For example, you can deploy any of the methodologies described in
+ tutorials on a different dataset. You can also upload your own data with
+ the 'Upload' button (via the menu bar on top, under the 'Publish' menu),
+ or directly access datasets in the NOMAD Archive. Make sure to learn
+ how to access the data in the NOMAD Archive, which is explained in the
+ tutorial accessible from the 'Query the Archive' button.
+
+
+
+
+
+
+
+
+
+
+ What is the NOMAD Artificial-Intelligence Toolkit?
+
+
+ The preparation, synthesis, and characterization of new materials is a
+ complex and costly aspect of materials design. The number of possible
+ materials is practically infinite, about 200,000 materials are “known”
+ to exist. But the basic properties (e.g., optical gap, elasticity
+ constants, plasticity, piezoelectric tensors, conductivity, etc.) have
+ been determined for very few of them. NOMAD develops and provides a
+ big set of tools - the Artificial-Intelligence Toolkit - using the
+ latest artificial-intelligence approaches (including machine-learning,
+ compressed sensing, and data mining) that make it possible to sort all
+ available material data, to identify correlations and structures, and
+ to detect trends and anomalies. Thus, the Artificial Intelligence Toolkit
+ enables scientists and engineers to decide which materials are useful for
+ specific applications or which new materials should be the focus of future
+ studies.
+
+
+
+ 
+
+
+
+
+ How to get started
+
+
+ Introduction to the scope of the NOMAD Artificial-Intelligence toolkit.
+
+
+
+ The NOMAD Artificial-Intelligence Toolkit is very accessible. Watch this video and
+ learn more about its features.
+
+
+
+
+
+ Read about us!
+
+
+ By clicking on the image below, you will access a Nature Reviews paper
+ which gives an introduction to the NOMAD Artificial-Intelligence Toolkit.
+
+
+ 
+
+
+
+
+ Access the tutorials
+
+ Ready to start? Click on one of the options below. If you're new, we
+ suggest starting with the tutorials.
+
+
-
- option}
- style={{ width: '100%', marginBottom: 8 }}
- renderInput={params => (
- option}
- style={{ width: '100%', marginBottom: 8 }}
- renderInput={params => (
- (
-
+
+
+ 
+
+
+
+ 
+
+
+ 
+
+
+ 
+
+
+
+
+
+
+
+
+
+
+
+
+
}
diff --git a/gui/src/components/aitoolkit/AccordionsList.js b/gui/src/components/aitoolkit/AccordionsList.js
new file mode 100644
index 0000000000000000000000000000000000000000..381468e03b96f03f2d23bf14d40a6e9487fab1db
--- /dev/null
+++ b/gui/src/components/aitoolkit/AccordionsList.js
@@ -0,0 +1,287 @@
+/*
+ * Copyright The NOMAD Authors.
+ *
+ * This file is part of NOMAD. See https://nomad-lab.eu for further info.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import React from 'react'
+import {
+ Divider,
+ Typography,
+ Link,
+ AccordionActions,
+ Button,
+ Grid,
+ makeStyles
+} from '@material-ui/core'
+import MuiAccordion from '@material-ui/core/Accordion'
+import MuiAccordionSummary from '@material-ui/core/AccordionSummary'
+import MuiAccordionDetails from '@material-ui/core/AccordionDetails'
+import { styled } from '@material-ui/core/styles'
+import ArrowForwardIosSharpIcon from '@material-ui/icons/ArrowForwardIosSharp'
+import ExpandMoreIcon from '@material-ui/icons/ExpandMore'
+import Markdown from '../Markdown'
+import { StringParam, useQueryParam } from 'use-query-params'
+import AccessIcon from '../../images/AIT_ico_bd_link_external_big.svg'
+import WatchIcon from '../../images/AIT_ico_bd_youtube.svg'
+import PdfIcon from '../../images/AIT_ico_bd_link_pdf.svg'
+import DoiIcon from '../../images/AIT_ico_bd_link_doi.svg'
+
+const useStyles = makeStyles(theme => ({
+ tutorialTitleGrid: {
+ marginRight: '40px'
+ },
+ tutorialTitleText: {
+ fontWeight: theme.typography.fontWeightMedium,
+ fontSize: '28px',
+ color: '#2A3C67',
+ lineHeight: '30px'
+ },
+ fieldText: {
+ color: '#2A3C67'
+ },
+ linkAuthors: {
+ color: '#2A3C67',
+ cursor: 'pointer',
+ lineHeight: '20px',
+ fontSize: '16px'
+ },
+ tutorialDescriptionGrid: {
+ marginLeft: '50px'
+ },
+ tutorialDescriptionText: {
+ color: '#2A3C67',
+ fontSize: '18px'
+ },
+ keywordsGrid: {
+ marginLeft: '80px'
+ },
+ linkKeywords: {
+ border: '1.5px solid #00DFE0',
+ lineHeight: '35px',
+ color: '#2A3C67',
+ cursor: 'pointer',
+ fontStyle: 'normal',
+ fontSize: '16px'
+ },
+ tutorialActions: {
+ marginLeft: '50px'
+ },
+ tutorialResources: {
+ marginTop: '-17px',
+ marginLeft: '-6px'
+ }
+}))
+
+const Accordion = styled((props) => (
+
+
+
+
+
+
+
+
+
+
+
+ Reproduce published results
+
+
+
+
+ Reproducibility is key in scientific research. The advent of AI-driven big-data analytics has
+ introduced a new possible source of uncertainty in scientific communication: The AI workflow are
+ typically complex and there is no standard way to describe them. A solution is offered by the
+ NOMAD AI toolkit, which hosts open-access notebooks that allow to reproduce result published in
+ scientific journals. Explore them below
+
+
+
+ 
+
+
+
+
+
+ Filter Tutorials
+
+
+
+ option}
+ renderInput={params => (
+
+
+ (
+
+
+ option}
+ renderInput={params => (
+
+
+
+
+
+
+
+
+
+
+
+
+ Explore the basics of AI
+
+
+ Recent applications of artificial intelligence in science build on top
+ of solid methodologies that have been being developed over the last decades.
+ Exploring the following tutorials, you can learn the basics of AI to
+ better understand their latest applications in materials science.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 
+
+
+
+
+}
diff --git a/gui/src/components/aitoolkit/TutorialsPage.js b/gui/src/components/aitoolkit/TutorialsPage.js
new file mode 100644
index 0000000000000000000000000000000000000000..f653b395cf88a7ab95c9f62ffc697c0172e28a05
--- /dev/null
+++ b/gui/src/components/aitoolkit/TutorialsPage.js
@@ -0,0 +1,415 @@
+/*
+ * Copyright The NOMAD Authors.
+ *
+ * This file is part of NOMAD. See https://nomad-lab.eu for further info.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+import React, { useMemo, useState } from 'react'
+import {
+ Box,
+ Button,
+ Grid,
+ TextField,
+ Typography,
+ Divider,
+ IconButton,
+ makeStyles
+} from '@material-ui/core'
+import { Link } from 'react-router-dom'
+import Autocomplete from '@material-ui/lab/Autocomplete'
+import { StringParam, useQueryParams } from 'use-query-params'
+import TutorialsIcon from '../../images/AIT_ico_bp_tutorial.svg'
+import ArrowIcon from '../../images/AIT_ico_bd_link_go_to.svg'
+import ReproduceIcon from '../../images/AIT_ico_bb_replicate.svg'
+import AccordionsList from './AccordionsList'
+import FigureAI from '../../images/AIT_illu_AIT.svg'
+import tutorials from '../../toolkitMetadata'
+
+export const useStyles = makeStyles(theme => ({
+ root: {
+ margin: theme.spacing(3),
+ width: '100%',
+ marginLeft: 'auto',
+ marginRight: 'auto',
+ maxWidth: '1052px',
+ marginBottom: '150px'
+ },
+ sectionIcon: {
+ marginTop: theme.spacing(3)
+ },
+ sectionTitle: {
+ marginBottom: theme.spacing(1),
+ marginLeft: theme.spacing(2),
+ marginTop: '105px'
+ },
+ title: {
+ color: '#2A3C67',
+ fontSize: '35px',
+ marginLeft: '-10px',
+ fontWeight: theme.typography.fontWeightMedium,
+ marginTop: '-70px'
+ },
+ deck: {
+ color: '#2A3C67',
+ fontSize: '22px',
+ marginTop: '20px',
+ lineHeight: '30px',
+ marginLeft: '-10px',
+ width: '518px'
+ },
+ icon: {
+ height: '371px',
+ marginTop: '-20px',
+ marginLeft: '100px'
+ },
+ filter: {
+ fontWeight: theme.typography.fontWeightMedium,
+ color: '#2A3C67',
+ fontSize: '20px',
+ marginTop: '60px',
+ marginLeft: '0px'
+ },
+ autocomplete: {
+ height: 'auto',
+ color: '#2A3C67',
+ border: '3px solid #00DFE0',
+ borderRadius: '10px 10px 10px 10px',
+ marginTop: '10px',
+ marginLeft: '0px',
+ width: '240px'
+ },
+ tutorialsList: {
+ marginTop: '50px'
+ },
+ textLevel: {
+ textAlign: 'left',
+ color: '#2A3C67',
+ fontSize: '22px',
+ height: '22px',
+ marginTop: '-16px'
+ },
+ titleSecondary: {
+ fontWeight: 'bold',
+ color: '#00DFE0',
+ fontSize: '35px',
+ marginLeft: '-10px'
+ },
+ bottomButton: {
+ color: '#F3F2F5',
+ backgroundColor: '#F3F2F5',
+ borderRadius: '30px',
+ width: '242px',
+ height: '70px',
+ textAlign: 'center',
+ align: 'center',
+ marginTop: '40px',
+ textTransform: 'none',
+ fontSize: '12pt',
+ lineHeight: '20px'
+ },
+ bottomButtonText: {
+ color: '#2A3C67',
+ fontWeight: theme.typography.fontWeightMedium
+ },
+ bottomIcon: {
+ marginTop: '80px',
+ marginLeft: '120px'
+ }
+}))
+
+export default function TutorialsPage() {
+ const styles = useStyles()
+ const [queryParameters, setQueryParameters] = useQueryParams({
+ author: StringParam, system: StringParam, method: StringParam, filterString: StringParam
+ })
+
+ const filter = tutorial => {
+ const {author, system, method} = queryParameters
+ if (author && tutorial.authors.indexOf(author) === -1) {
+ return false
+ }
+ if (system && tutorial.labels.application_system.indexOf(system) === -1) {
+ return false
+ }
+ if (method && tutorial.labels.ai_methods.indexOf(method) === -1) {
+ return false
+ }
+ return true
+ }
+
+ const tutorials_list = tutorials.tutorials.filter(tutorial => tutorial.labels.category[0] === 'beginner_tutorial' ||
+ tutorial.labels.category[0] === 'intermediate_tutorial' || tutorial.labels.category[0] === 'query_tutorial')
+
+ const tutorials_list_beginner = tutorials_list.filter(tutorial => tutorial.labels.category[0] === 'beginner_tutorial')
+
+ const tutorials_list_intermediate = tutorials_list.filter(tutorial => tutorial.labels.category[0] === 'intermediate_tutorial')
+
+ const tutorials_list_query = tutorials_list.filter(tutorial => tutorial.labels.category[0] === 'query_tutorial')
+
+ const {authors, systems, methods} = useMemo(() => {
+ const authors = {}
+ const systems = {}
+ const methods = {}
+ tutorials_list.forEach(tutorial => {
+ tutorial.key = tutorial.title.replace(/\W/gm, '_').toLowerCase()
+ tutorial.authors.forEach(i => { authors[i] = i })
+ tutorial.labels.application_system.forEach(i => { systems[i] = i })
+ tutorial.labels.ai_methods.forEach(i => { methods[i] = i })
+ }
+ )
+ return {
+ authors: Object.keys(authors).sort(),
+ systems: Object.keys(systems).sort(),
+ methods: Object.keys(methods).sort()
+ }
+ }, [tutorials_list])
+
+ const [valAuthor, setAuthor] = useState({})
+ const [valSystem, setSystem] = useState({})
+ const [valMethod, setMethod] = useState({})
+
+ return
+
+
+
+
+
+
+
+
+
+
+ Learn from tutorials
+
+
+
+
+ We develop and implement methods that identify correlations and
+ structure in big data of materials. This will enable scientists and
+ engineers to decide which materials are useful for specific
+ applications or which new materials should be the focus of future
+ studies. The following BEGINNER and INTERMEDIATE LEVEL tutorials are designed to get started with the
+ AI Toolkit.
+
+
+
+
+
+
+
+
+
+ Filter Tutorials
+
+
+
+ option}
+ renderInput={params => (
+
+
+ (
+
+
+ option}
+ renderInput={params => (
+
+
+
+
+ {tutorials_list_beginner.some(tutorial => filter(tutorial)) &&
+
+
+
+ BEGINNER LEVEL
+
+
+
+
+
+ }
+
+
+ {tutorials_list_intermediate.some(tutorial => filter(tutorial)) &&
+
+
+
+ INTERMEDIATE LEVEL
+
+
+
+
+
+ }
+
+
+
+
+ {tutorials_list_query.some(tutorial => filter(tutorial)) &&
+
+
+
+ TOOLKIT INFRASTRUCTURE
+
+
+
+
+
+ }
+
+
+
+
+
+
+
+ Next advanced level
+
+
+ After learning the basics of artificial-intelligence tools, you can apply the latest
+ AI developments to timely problems in materials science. These
+ outstanding applications allow to reproduce results that have been
+ published recently in scientific journals.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+}
diff --git a/gui/src/components/nav/MainMenu.js b/gui/src/components/nav/MainMenu.js
index b4b440368f0f6e20926f49d69bff38a8cc49953d..1f0712e8798b21bef94bd4d2bc17c12b65e92a07 100644
--- a/gui/src/components/nav/MainMenu.js
+++ b/gui/src/components/nav/MainMenu.js
@@ -146,7 +146,7 @@ export default function MainMenu() {
}>
{(!oasis && aitoolkitEnabled)
?
- {section.tutorials.map(tutorial => {
- const key = tutorial.key
- return setExpanded(expanded === key ? null : key)}
- className={classes.tutorial}
+ return
+
+
+
+
+
+ Artificial-Intelligence Tools for Materials Science
+
+
+
+
+ Find new Patterns and Trends in Materials Science Big Data
+
+
+
+
+
+ 
+
+
+
+
- {tutorial.title}
-
-
-
- {tutorial.authors
- .map(name => {
- const label = name.split(',').reverse().join(' ')
- return setQueryParameters({
- ...emptyQuery,
- author: queryParameters.author === name ? null : name
- })}
- >
- {label}
-
- }).reduce((prev, curr) => [prev, ', ', curr])
- }
-
-
- {tutorial.description}
-
-
- keywords: {tutorial.labels.application_keyword
- .map(keyword => (
- setQueryParameters({
- ...emptyQuery,
- keyword: queryParameters.keyword === keyword ? null : keyword
- })}
- >
- {keyword}
-
- )).reduce((prev, curr) => [prev, ', ', curr])
- }
-
-
- method: {tutorial.labels.data_analytics_method
- .map(method => (
- setQueryParameters({
- ...emptyQuery,
- method: queryParameters.method === method ? null : method
- })}
- >
- {method}
-
- )).reduce((prev, curr) => [prev, ', ', curr])
- }
+ Query the Archive
+
+
+
+
+
+
+
+
+
+
+
+ 
+
+
+
+
+ View tutorials
+
+
+
+
+
+
+
+
+
+
+
+ 
+
+
+
+
+ Published results
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 
+
+
+
+
+ Get to work
-
-
-
-
-
-
- })}
-
+
+
+
- ))}
+
+
+
+ setExpanded(expanded === tutorial.key ? null : tutorial.key)}
+ elevation={0}
+ >
+
+
+
+ {tutorial.title}
+
+
+
+
+ {Authors: }
+ {tutorial.authors
+ .map(name => {
+ const label = name.split(',').reverse().join(' ')
+ return {
+ props.setQueryParameters({
+ ...props.emptyQuery,
+ author: props.queryParameters.author === name ? null : name
+ })
+ props.setAuthor(name)
+ }}
+ >
+ {label}
+
+ }).reduce((prev, curr) => [prev, ' | ', curr])
+ }
+
+
+
+
+
+
+
+
+ {tutorial.description}
+
+
+
+
+ AI methods:
+
+
+ {tutorial.labels.ai_methods
+ .map(method => (
+ {
+ props.setQueryParameters({
+ ...props.emptyQuery,
+ method: props.queryParameters.method === method ? null : method
+ })
+ props.setMethod(method)
+ }}
+ >
+ {method}
+
+ )).reduce((prev, curr) => [prev, ' ', curr])
+ }
+
+
+ System:
+
+
+ {tutorial.labels.application_system
+ .map(system => (
+ {
+ props.setQueryParameters({
+ ...props.emptyQuery,
+ system: props.queryParameters.system === system ? null : system
+ })
+ props.setSystem(system)
+ }}
+ >
+ {system}
+
+ )).reduce((prev, curr) => [prev, ' ', curr])
+ }
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ {tutorial.link_paper &&
+
+
+
+ Additional Resources:
+
+
+
+
+
+
+
+
+
+ }
+
+
+
+
+ )))
+}
+
+export default AccordionsList
diff --git a/gui/src/components/aitoolkit/ReproducePage.js b/gui/src/components/aitoolkit/ReproducePage.js
new file mode 100644
index 0000000000000000000000000000000000000000..09d803d12ba34f7eab7add711d16847b055689a0
--- /dev/null
+++ b/gui/src/components/aitoolkit/ReproducePage.js
@@ -0,0 +1,252 @@
+/*
+ * Copyright The NOMAD Authors.
+ *
+ * This file is part of NOMAD. See https://nomad-lab.eu for further info.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import React, { useMemo, useState } from 'react'
+import { useStyles } from './TutorialsPage'
+import {
+ Button,
+ Grid,
+ TextField,
+ Typography,
+ Divider,
+ IconButton
+} from '@material-ui/core'
+import { Link } from 'react-router-dom'
+import tutorials from '../../toolkitMetadata'
+import { StringParam, useQueryParams } from 'use-query-params'
+import Autocomplete from '@material-ui/lab/Autocomplete'
+import TutorialsIcon from '../../images/AIT_ico_bb_tutorial.svg'
+import ArrowIcon from '../../images/AIT_ico_bd_link_go_to.svg'
+import ReproduceIcon from '../../images/AIT_ico_bp_replicate.svg'
+import AccordionsList from './AccordionsList'
+import FigureAI from '../../images/AIT_illu_AIT.svg'
+
+export default function ReproducePage() {
+ const styles = useStyles()
+ const [queryParameters, setQueryParameters] = useQueryParams({
+ author: StringParam, system: StringParam, method: StringParam, filterString: StringParam
+ })
+
+ const filter = tutorial => {
+ const {author, system, method} = queryParameters
+ if (author && tutorial.authors.indexOf(author) === -1) {
+ return false
+ }
+ if (system && tutorial.labels.application_system.indexOf(system) === -1) {
+ return false
+ }
+ if (method && tutorial.labels.ai_methods.indexOf(method) === -1) {
+ return false
+ }
+ return true
+ }
+
+ const tutorials_list_advanced = tutorials.tutorials.filter(tutorial => tutorial.labels.category[0] === 'advanced_tutorial')
+
+ const {authors, systems, methods} = useMemo(() => {
+ const authors = {}
+ const systems = {}
+ const methods = {}
+ tutorials_list_advanced.forEach(tutorial => {
+ tutorial.key = tutorial.title.replace(/\W/gm, '_').toLowerCase()
+ tutorial.authors.forEach(i => { authors[i] = i })
+ tutorial.labels.application_system.forEach(i => { systems[i] = i })
+ tutorial.labels.ai_methods.forEach(i => { methods[i] = i })
+ }
+ )
+ return {
+ authors: Object.keys(authors).sort(),
+ systems: Object.keys(systems).sort(),
+ methods: Object.keys(methods).sort()
+ }
+ }, [tutorials_list_advanced])
+
+ const [valAuthor, setAuthor] = useState({})
+ const [valSystem, setSystem] = useState({})
+ const [valMethod, setMethod] = useState({})
+
+ return
+ { tutorial.link_video && }
+
+
+ {tutorial.link_paper && }
+
+
+ {tutorial.link_paper && }
+
+
diff --git a/gui/src/components/nav/Routes.js b/gui/src/components/nav/Routes.js
index b9136d16c6ab5f5fef78199fa3ea3b60aaffeecb..62273599d4d7adb2f9dfa37596a6d33262810faf 100644
--- a/gui/src/components/nav/Routes.js
+++ b/gui/src/components/nav/Routes.js
@@ -21,6 +21,8 @@ import { Route, useLocation } from 'react-router-dom'
import About from '../About'
import APIs from '../APIs'
import AIToolkitPage from '../aitoolkit/AIToolkitPage'
+import TutorialsPage from '../aitoolkit/TutorialsPage'
+import ReproducePage from '../aitoolkit/ReproducePage'
import { MetainfoPage, help as metainfoHelp } from '../archive/MetainfoBrowser'
import ResolveDOI from '../dataset/ResolveDOI'
import DatasetPage from '../DatasetPage'
@@ -157,11 +159,23 @@ const routeSpecs = [
component: MetainfoPage
},
{
- path: '/aitoolkit',
+ path: '/aitoolkit/main',
title: 'Artificial Intelligence Toolkit',
- navPath: 'analyze/aitoolkit',
+ navPath: 'analyze/aitoolkit/main',
component: AIToolkitPage
},
+ {
+ path: '/aitoolkit/tutorials',
+ title: 'Artificial Intelligence Toolkit',
+ navPath: 'analyze/aitoolkit/tutorials',
+ component: TutorialsPage
+ },
+ {
+ path: '/aitoolkit/reproduce',
+ title: 'Artificial Intelligence Toolkit',
+ navPath: 'analyze/aitoolkit/reproduce',
+ component: ReproducePage
+ },
{
exact: true,
path: '/apis',
diff --git a/gui/src/images/AIT_bg_title.jpg b/gui/src/images/AIT_bg_title.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..511cde7740e76186408a15dc766a2fb7c6dff10a
Binary files /dev/null and b/gui/src/images/AIT_bg_title.jpg differ
diff --git a/gui/src/images/AIT_ico_bb_query.svg b/gui/src/images/AIT_ico_bb_query.svg
new file mode 100644
index 0000000000000000000000000000000000000000..f88f33ce3ec0facf0eb9c2713abe653906bb2ac6
--- /dev/null
+++ b/gui/src/images/AIT_ico_bb_query.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_ico_bb_replicate.svg b/gui/src/images/AIT_ico_bb_replicate.svg
new file mode 100644
index 0000000000000000000000000000000000000000..7970f37dd38672776780545917c2b97e3970dfe2
--- /dev/null
+++ b/gui/src/images/AIT_ico_bb_replicate.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_ico_bb_tutorial.svg b/gui/src/images/AIT_ico_bb_tutorial.svg
new file mode 100644
index 0000000000000000000000000000000000000000..cd40c353bd07e896cc58ef701801ddb39691e8f7
--- /dev/null
+++ b/gui/src/images/AIT_ico_bb_tutorial.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_ico_bb_work.svg b/gui/src/images/AIT_ico_bb_work.svg
new file mode 100644
index 0000000000000000000000000000000000000000..6a3dfc30ca90ce9d33f1773e85918435a2fedea9
--- /dev/null
+++ b/gui/src/images/AIT_ico_bb_work.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_ico_bd_info_circle.svg b/gui/src/images/AIT_ico_bd_info_circle.svg
new file mode 100644
index 0000000000000000000000000000000000000000..7793cb36dc1bea644ee36110ae91b209be8cb187
--- /dev/null
+++ b/gui/src/images/AIT_ico_bd_info_circle.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_ico_bd_link_doi.svg b/gui/src/images/AIT_ico_bd_link_doi.svg
new file mode 100644
index 0000000000000000000000000000000000000000..f170ac2f84476e487f4e9bceb422fd1689540b62
--- /dev/null
+++ b/gui/src/images/AIT_ico_bd_link_doi.svg
@@ -0,0 +1,7 @@
+
diff --git a/gui/src/images/AIT_ico_bd_link_external_big.svg b/gui/src/images/AIT_ico_bd_link_external_big.svg
new file mode 100644
index 0000000000000000000000000000000000000000..b2d331e8252c72296ad70f5b173a2d4a4dd3b71b
--- /dev/null
+++ b/gui/src/images/AIT_ico_bd_link_external_big.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_ico_bd_link_go_to.svg b/gui/src/images/AIT_ico_bd_link_go_to.svg
new file mode 100644
index 0000000000000000000000000000000000000000..515dbec13d706302e3f7f91fd3e77db35fcce40a
--- /dev/null
+++ b/gui/src/images/AIT_ico_bd_link_go_to.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_ico_bd_link_pdf.svg b/gui/src/images/AIT_ico_bd_link_pdf.svg
new file mode 100644
index 0000000000000000000000000000000000000000..3b7df00b341b0fcd3d36d830f8ff512ebeda34ed
--- /dev/null
+++ b/gui/src/images/AIT_ico_bd_link_pdf.svg
@@ -0,0 +1,9 @@
+
diff --git a/gui/src/images/AIT_ico_bd_youtube.svg b/gui/src/images/AIT_ico_bd_youtube.svg
new file mode 100644
index 0000000000000000000000000000000000000000..60df30297a409ca37f2e067014ad5b70fc0f9a31
--- /dev/null
+++ b/gui/src/images/AIT_ico_bd_youtube.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_ico_bp_query.svg b/gui/src/images/AIT_ico_bp_query.svg
new file mode 100644
index 0000000000000000000000000000000000000000..474547487c052fbea176cc33f0e1f65be797e123
--- /dev/null
+++ b/gui/src/images/AIT_ico_bp_query.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_ico_bp_replicate.svg b/gui/src/images/AIT_ico_bp_replicate.svg
new file mode 100644
index 0000000000000000000000000000000000000000..0a1f1075ea8d07665145b04d47debbdefc6d1b3d
--- /dev/null
+++ b/gui/src/images/AIT_ico_bp_replicate.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_ico_bp_tutorial.svg b/gui/src/images/AIT_ico_bp_tutorial.svg
new file mode 100644
index 0000000000000000000000000000000000000000..2050512268bd82d2d1b3134448bee1bb4bc585fb
--- /dev/null
+++ b/gui/src/images/AIT_ico_bp_tutorial.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_ico_bp_work.svg b/gui/src/images/AIT_ico_bp_work.svg
new file mode 100644
index 0000000000000000000000000000000000000000..8ef95da19a3f7e45da3bcb427da613727a58a96d
--- /dev/null
+++ b/gui/src/images/AIT_ico_bp_work.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/gui/src/images/AIT_illu_AIT.svg b/gui/src/images/AIT_illu_AIT.svg
new file mode 100644
index 0000000000000000000000000000000000000000..1e8d3b2651719402d492270e50b775908b44b11f
--- /dev/null
+++ b/gui/src/images/AIT_illu_AIT.svg
@@ -0,0 +1,27 @@
+
diff --git a/gui/src/images/AIT_img_natrev.jpg b/gui/src/images/AIT_img_natrev.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..05c98deb9ba7da3c1b80b5c17f0b784e6c956f17
Binary files /dev/null and b/gui/src/images/AIT_img_natrev.jpg differ
diff --git a/gui/src/images/AIT_slide_nature.png b/gui/src/images/AIT_slide_nature.png
new file mode 100644
index 0000000000000000000000000000000000000000..2c23e07e5ec579c6e73d2be230956f0f2305e8ea
Binary files /dev/null and b/gui/src/images/AIT_slide_nature.png differ
diff --git a/gui/src/components/about.svg b/gui/src/images/about.svg
similarity index 100%
rename from gui/src/components/about.svg
rename to gui/src/images/about.svg
diff --git a/gui/src/index.css b/gui/src/index.css
index b4cc7250b98cb3f1a2dd5bec134296c6942344d9..673b8794313a28b8f03f7374c7ea197cc4320f46 100644
--- a/gui/src/index.css
+++ b/gui/src/index.css
@@ -2,4 +2,4 @@ body {
margin: 0;
padding: 0;
font-family: sans-serif;
-}
+}
\ No newline at end of file
diff --git a/gui/src/toolkitMetadata.json b/gui/src/toolkitMetadata.json
deleted file mode 100644
index b25582e65c9e5afa52ca5ea8911d4c5a3e676044..0000000000000000000000000000000000000000
--- a/gui/src/toolkitMetadata.json
+++ /dev/null
@@ -1,426 +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": [
- "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/admin/admin.py b/nomad/cli/admin/admin.py
index 8e779e890e85c8a44387243899fec532a5513982..10e1457a0a25a0a53e6d6b3e6d3fd5b469373996 100644
--- a/nomad/cli/admin/admin.py
+++ b/nomad/cli/admin/admin.py
@@ -727,22 +727,18 @@ def gui_config():
env_js_file = os.path.join(run_gui_folder, 'env.js')
if not os.path.exists(env_js_file):
with open(env_js_file, 'wt') as f:
- f.write(('''
-window.nomadEnv = {
- 'appBase': '%s',
+ f.write(f'''
+window.nomadEnv = {{
+ 'appBase': '{config.services.api_base_path}',
'keycloakBase': 'https://nomad-lab.eu/fairdi/keycloak/auth/',
- 'keycloakRealm': '%s',
- 'keycloakClientId': '%s',
+ 'keycloakRealm': '{config.keycloak.realm_name}',
+ 'keycloakClientId': '{config.keycloak.client_id}',
'debug': false,
'matomoEnabled': false,
- 'encyclopediaEnabled': true,
- 'oasis': %s
-};''' % (
- config.services.api_base_path,
- config.keycloak.realm_name,
- config.keycloak.client_id,
- 'true' if config.keycloak.oasis else 'false'
- )))
+ 'encyclopediaEnabled': {'true' if config.encyclopedia_enabled else 'false'},
+ 'aitoolkitEnabled': {'true' if config.aitoolkit_enabled else 'false'},
+ 'oasis': {'true' if config.keycloak.oasis else 'false'}
+}};''')
# replace base path in all GUI files
source_file_globs = [
diff --git a/nomad/config.py b/nomad/config.py
index e85117b86582c25762e363797b252b6b98564162..12adcb57b11f92020a5a08fb4a7d5c04b9dce03f 100644
--- a/nomad/config.py
+++ b/nomad/config.py
@@ -316,6 +316,8 @@ process_reuse_parser = True
metadata_file_name = 'nomad'
metadata_file_extensions = ('json', 'yaml', 'yml')
enable_lazy_import = True
+encyclopedia_enabled = True
+aitoolkit_enabled = True
def normalize_loglevel(value, default_level=logging.INFO):
diff --git a/ops/helm/nomad/templates/nomad-configmap.yml b/ops/helm/nomad/templates/nomad-configmap.yml
index ccdebf7e0422c296df41f7c59e981ec2df2355a1..e1171d5bb91bc773405a9dbb335c1ac606321f91 100644
--- a/ops/helm/nomad/templates/nomad-configmap.yml
+++ b/ops/helm/nomad/templates/nomad-configmap.yml
@@ -22,6 +22,7 @@ data:
reprocess_unmatched: {{ .Values.reprocess_unmatched }}
reprocess_rematch: {{ .Values.reprocess_rematch }}
process_reuse_parser: {{ .Values.process_reuse_parser }}
+ aitoolkit_enabled: {{ .Values.aitoolkit.enabled }}
fs:
tmp: "{{ .Values.volumes.tmp }}"
prefix_size: {{ .Values.volumes.prefixSize }}
diff --git a/ops/helm/nomad/values.yaml b/ops/helm/nomad/values.yaml
index 3fcec299834d2b26d3b5867b44458636b17fbd3a..04121228a1c99b9a0d6325c21be526d30785bfc1 100644
--- a/ops/helm/nomad/values.yaml
+++ b/ops/helm/nomad/values.yaml
@@ -176,3 +176,6 @@ reprocess_match: false
datacite:
enabled: false
prefix: "10.17172"
+
+aitoolkit:
+ enabled: true
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 5ed88e0a3b01f2b36fc2783373ee529f8804aaae..6019c791f6f77db0e9c19b6ee8d37798f6aee3ac 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,7 +11,7 @@ future
nptyping
cachetools
docstring-parser
-Pint
+Pint==0.17
orjson==3.6.0
click
requests