From c15e9b6f1d831ab4dbe344af15d1dfc08f8d84d1 Mon Sep 17 00:00:00 2001 From: Adam Fekete Date: Wed, 22 Jan 2025 23:30:49 +0100 Subject: [PATCH] update schema --- notebooks/arise.archive.json | 4 ++-- notebooks/atomic-features.archive.json | 4 ++-- notebooks/clustering-tutorial.archive.json | 4 ++-- notebooks/cmlkit.archive.json | 4 ++-- notebooks/co2-sgd-tutorial.archive.json | 4 ++-- notebooks/compressed-sensing.archive.json | 4 ++-- notebooks/convolutional-nn.archive.json | 4 ++-- notebooks/decision-tree.archive.json | 4 ++-- notebooks/descriptor-role.archive.json | 4 ++-- .../domain-of-applicability.archive.json | 4 ++-- notebooks/dos-similarity-search.archive.json | 4 ++-- notebooks/error-estimates.archive.json | 4 ++-- notebooks/exploratory-analysis.archive.json | 4 ++-- notebooks/gap-si-surface.archive.json | 4 ++-- notebooks/grain-boundaries.archive.json | 4 ++-- notebooks/hierarchical-sisso.archive.json | 4 ++-- notebooks/kaggle-competition.archive.json | 4 ++-- notebooks/krr4mat.archive.json | 4 ++-- notebooks/nn-regression.archive.json | 4 ++-- .../perovskite-tolerance-factor.archive.json | 4 ++-- notebooks/query-nomad-archive.archive.json | 4 ++-- ...ys-oxygen-reduction-evolution.archive.json | 4 ++-- .../sgd-propylene-oxidation-hte.archive.json | 4 ++-- notebooks/soap-atomic-charges.archive.json | 4 ++-- notebooks/tcmi.archive.json | 4 ++-- notebooks/tetradymite-PRM2020.archive.json | 4 ++-- notebooks/tutorial_stats.ipynb | 24 +++++++++++++++---- pyproject.toml | 2 +- src/nomad_aitoolkit/apps/__init__.py | 4 ++-- src/nomad_aitoolkit/schema/__init__.py | 15 ++++++------ 30 files changed, 81 insertions(+), 68 deletions(-) diff --git a/notebooks/arise.archive.json b/notebooks/arise.archive.json index 756b4cc..4a969db 100644 --- a/notebooks/arise.archive.json +++ b/notebooks/arise.archive.json @@ -4,7 +4,7 @@ "name": "ARISE - Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning", "description": "In this tutorial, we give an introduction to ARISE (ARtificial-Intelligence-based Structure Evaluation), a powerful Bayesian-deep-neural-network tool for the recognition of atomistic structures (A. Leitherer, A. Ziletti, and L.M. Ghiringhelli, Nat. Commun. 12, 6234, 2021). ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates. By applying unsupervised learning to the internal neural-network representations, one can reveal grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties.", "date": "2021-03-22", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Supervised learning" @@ -34,7 +34,7 @@ "name": "SOAP" } ], - "systems": [ + "applications": [ { "name": "Grain boundaries" }, diff --git a/notebooks/atomic-features.archive.json b/notebooks/atomic-features.archive.json index 232c662..d3a4d23 100644 --- a/notebooks/atomic-features.archive.json +++ b/notebooks/atomic-features.archive.json @@ -4,13 +4,13 @@ "name": "Atomic-features-package usage demonstration", "description": "In this tutorial, we show how the atomic-features-package can be accessed and used to explore the atomic features form various sources and to prepare the input features for machine-learning studies.", "date": "2021-12-07", - "category": "query_tutorial", + "category": "Query tutorial", "methods": [ { "name": "" } ], - "systems": [ + "applications": [ { "name": "Atoms" } diff --git a/notebooks/clustering-tutorial.archive.json b/notebooks/clustering-tutorial.archive.json index 5ea359e..ccfb8cb 100644 --- a/notebooks/clustering-tutorial.archive.json +++ b/notebooks/clustering-tutorial.archive.json @@ -4,7 +4,7 @@ "name": "Introduction to clustering", "description": "In this tutorial, we introduce to the most popular clustering algorithms. We focus on partitioning, hierarchical and density-based clustering algorithms. The methods are tested on synthetic datasets of increasing complexity", "date": "2021-01-21", - "category": "beginner_tutorial", + "category": "Beginner tutorial", "methods": [ { "name": "Unsupervised learning" @@ -25,7 +25,7 @@ "name": "HDBSCAN" } ], - "systems": [ + "applications": [ { "name": "Synthetic data" } diff --git a/notebooks/cmlkit.archive.json b/notebooks/cmlkit.archive.json index 4ae2ff6..328434e 100644 --- a/notebooks/cmlkit.archive.json +++ b/notebooks/cmlkit.archive.json @@ -4,7 +4,7 @@ "name": "cmlkit: Toolkit for Machine Learning in Materials Science and Quantum Chemistry", "description": "In this tutorial, we will get to know cmlkit, a python package for specifying, evaluating, and optimising machine learning models, and use it to compete in the Nomad 2018 Kaggle challenge.", "date": "2021-01-14", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Supervised learning" @@ -25,7 +25,7 @@ "name": "Symmetry functions" } ], - "systems": [ + "applications": [ { "name": "Transparent conducting oxides" } diff --git a/notebooks/co2-sgd-tutorial.archive.json b/notebooks/co2-sgd-tutorial.archive.json index 86ad11a..320d37f 100644 --- a/notebooks/co2-sgd-tutorial.archive.json +++ b/notebooks/co2-sgd-tutorial.archive.json @@ -4,7 +4,7 @@ "name": "Subgroup discovery of catalysts\u2019 genes for carbon-dioxide activation on semiconductor oxides", "description": "In this interactive tutorial we show the application of subgroup discovery for the search for indicators of carbond-dioxide activation with the aim of its further conversion.", "date": "2021-08-26", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Subgroup discovery" @@ -13,7 +13,7 @@ "name": "Decision tree" } ], - "systems": [ + "applications": [ { "name": "CO2 activation" }, diff --git a/notebooks/compressed-sensing.archive.json b/notebooks/compressed-sensing.archive.json index b80ccd3..f5acc9d 100644 --- a/notebooks/compressed-sensing.archive.json +++ b/notebooks/compressed-sensing.archive.json @@ -4,7 +4,7 @@ "name": "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.", "date": "2020-09-20", - "category": "beginner_tutorial", + "category": "Beginner tutorial", "methods": [ { "name": "Supervised learning" @@ -34,7 +34,7 @@ "name": "Atomic features" } ], - "systems": [ + "applications": [ { "name": "Octet binaries" } diff --git a/notebooks/convolutional-nn.archive.json b/notebooks/convolutional-nn.archive.json index f0becec..ae42d48 100644 --- a/notebooks/convolutional-nn.archive.json +++ b/notebooks/convolutional-nn.archive.json @@ -4,7 +4,7 @@ "name": "Introduction to convolutional neural networks", "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.", "date": "2021-01-29", - "category": "intermediate_tutorial", + "category": "Intermediate tutorial", "methods": [ { "name": "Supervised learning" @@ -22,7 +22,7 @@ "name": "Attentive response map" } ], - "systems": [ + "applications": [ { "name": "Images" } diff --git a/notebooks/decision-tree.archive.json b/notebooks/decision-tree.archive.json index e8187aa..9cdf1c6 100644 --- a/notebooks/decision-tree.archive.json +++ b/notebooks/decision-tree.archive.json @@ -4,7 +4,7 @@ "name": "Introduction to decision-trees methods", "description": "In this tutorial we will introduce decision trees. We go through a toy model introducing the SKLearn API. We then discuss step by step the different theoretical aspects of trees. We then move to training a regression tree and classification tree on different datasets related to materials science. We end the tutorial by covering random forests and bagging classfiers.", "date": "2020-12-08", - "category": "beginner_tutorial", + "category": "Beginner tutorial", "methods": [ { "name": "Supervised learning" @@ -28,7 +28,7 @@ "name": "Atomic features" } ], - "systems": [ + "applications": [ { "name": "Images" }, diff --git a/notebooks/descriptor-role.archive.json b/notebooks/descriptor-role.archive.json index 357116c..f07ff00 100644 --- a/notebooks/descriptor-role.archive.json +++ b/notebooks/descriptor-role.archive.json @@ -4,7 +4,7 @@ "name": "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.", "date": "2021-10-18", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Supervised learning" @@ -22,7 +22,7 @@ "name": "Atomic features" } ], - "systems": [ + "applications": [ { "name": "Octet binaries" }, diff --git a/notebooks/domain-of-applicability.archive.json b/notebooks/domain-of-applicability.archive.json index 2466c96..e625060 100644 --- a/notebooks/domain-of-applicability.archive.json +++ b/notebooks/domain-of-applicability.archive.json @@ -4,7 +4,7 @@ "name": "Identifying domains of applicability of machine-Learning models for materials science", "description": "In this tutorial, we present a method, based on subgroup discovery, for detecting domains of applicability (DA) of ML models within a materials class. The domain of applicability of an ML model is the region of input space where the model predicts the target property with the smallest uncertainty. The utility of this approach is demonstrated by analyzing three state-of-the-art ML models for predicting the formation energy of transparent conducting oxides.", "date": "2021-01-27", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Supervised learning" @@ -28,7 +28,7 @@ "name": "n-gram" } ], - "systems": [ + "applications": [ { "name": "Transparent conducting oxides" } diff --git a/notebooks/dos-similarity-search.archive.json b/notebooks/dos-similarity-search.archive.json index 4e3e476..26252be 100644 --- a/notebooks/dos-similarity-search.archive.json +++ b/notebooks/dos-similarity-search.archive.json @@ -4,7 +4,7 @@ "name": "Electronic density-of-states similarity search", "description": "This notebook shows how to compute the similarity of materials in terms of their electronic density-of-states (DOS), from data retrieved from the NOMAD Archive.", "date": "2022-03-30", - "category": "intermediate_tutorial", + "category": "Intermediate tutorial", "methods": [ { "name": "Similarity search" @@ -13,7 +13,7 @@ "name": "Fingerprint" } ], - "systems": [ + "applications": [ { "name": "Binaries" }, diff --git a/notebooks/error-estimates.archive.json b/notebooks/error-estimates.archive.json index d579041..56de27b 100644 --- a/notebooks/error-estimates.archive.json +++ b/notebooks/error-estimates.archive.json @@ -4,7 +4,7 @@ "name": "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.", "date": "2021-01-21", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Supervised learning" @@ -16,7 +16,7 @@ "name": "Linear least-squares regression" } ], - "systems": [ + "applications": [ { "name": "Binaries" }, diff --git a/notebooks/exploratory-analysis.archive.json b/notebooks/exploratory-analysis.archive.json index e94d873..fbdbff8 100644 --- a/notebooks/exploratory-analysis.archive.json +++ b/notebooks/exploratory-analysis.archive.json @@ -4,7 +4,7 @@ "name": "Introduction to exploratory analysis (unsupervised learning) of materials spaces", "description": "Exploratory analyses make use of unsupervised learning techniques to extract information from unknown datasets. In this tutorial, we make use of some of the most popular clustering and dimension reduction algorithms to analyze a dataset composed of 82 octet-binary compounds.", "date": "2021-02-04", - "category": "beginner_tutorial", + "category": "Beginner tutorial", "methods": [ { "name": "Clustering" @@ -37,7 +37,7 @@ "name": "MDS" } ], - "systems": [ + "applications": [ { "name": "Octet binaries" } diff --git a/notebooks/gap-si-surface.archive.json b/notebooks/gap-si-surface.archive.json index 8e69997..005a1d6 100644 --- a/notebooks/gap-si-surface.archive.json +++ b/notebooks/gap-si-surface.archive.json @@ -4,7 +4,7 @@ "name": "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 on the 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 the Si surface.", "date": "2020-06-18", - "category": "intermediate_tutorial", + "category": "Intermediate tutorial", "methods": [ { "name": "Supervised learning" @@ -25,7 +25,7 @@ "name": "Gaussian approximation potentials (GAP)" } ], - "systems": [ + "applications": [ { "name": "Silicon" }, diff --git a/notebooks/grain-boundaries.archive.json b/notebooks/grain-boundaries.archive.json index 4774f5a..02b0e82 100644 --- a/notebooks/grain-boundaries.archive.json +++ b/notebooks/grain-boundaries.archive.json @@ -4,7 +4,7 @@ "name": "Structure similarity and structure-property relationship: grain boundaries of alpha-Fe", "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.", "date": "2020-01-18", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Unsupervised learning" @@ -25,7 +25,7 @@ "name": "Gaussian mixture" } ], - "systems": [ + "applications": [ { "name": "Iron" }, diff --git a/notebooks/hierarchical-sisso.archive.json b/notebooks/hierarchical-sisso.archive.json index 620d3e5..1876b64 100644 --- a/notebooks/hierarchical-sisso.archive.json +++ b/notebooks/hierarchical-sisso.archive.json @@ -4,7 +4,7 @@ "name": "Hierarchical symbolic regression for identifying key physical parameters correlated with materials properties", "description": "In this notebook, we describe a hierarchical symbolic-regression approach for finding, based on data, analytical expressions relating materials properties to simpler physicochemical parameters associated with the underlying processes governing the properties.", "date": "2022-8-3", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Supervised learning" @@ -28,7 +28,7 @@ "name": "Atomic features" } ], - "systems": [ + "applications": [ { "name": "Bulk properties" }, diff --git a/notebooks/kaggle-competition.archive.json b/notebooks/kaggle-competition.archive.json index a99f70f..07fffb8 100644 --- a/notebooks/kaggle-competition.archive.json +++ b/notebooks/kaggle-competition.archive.json @@ -4,7 +4,7 @@ "name": "2018 NOMAD-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", "date": "2021-01-19", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Supervised learning" @@ -25,7 +25,7 @@ "name": "n-gram" } ], - "systems": [ + "applications": [ { "name": "Transparent conducting oxides" } diff --git a/notebooks/krr4mat.archive.json b/notebooks/krr4mat.archive.json index 9a84c9e..5dfee42 100644 --- a/notebooks/krr4mat.archive.json +++ b/notebooks/krr4mat.archive.json @@ -4,7 +4,7 @@ "name": "Introduction to kernel ridge regression for materials-property prediction", "description": "In this tutorial, we will explore the application of kernel ridge regression to the prediction of materials properties. We will begin with a largely informal, pragmatic introduction to kernel ridge regression, including a rudimentary implementation, in order to become familiar with the basic terminology and considerations. We will then discuss representations, and re-trace the NOMAD 2018 Kaggle challenge.", "date": "2020-12-15", - "category": "beginner_tutorial", + "category": "Beginner tutorial", "methods": [ { "name": "Supervised learning" @@ -19,7 +19,7 @@ "name": "SOAP" } ], - "systems": [ + "applications": [ { "name": "Transparent conducting oxides" } diff --git a/notebooks/nn-regression.archive.json b/notebooks/nn-regression.archive.json index 8b97a52..2bbcabb 100644 --- a/notebooks/nn-regression.archive.json +++ b/notebooks/nn-regression.archive.json @@ -4,7 +4,7 @@ "name": "Introduction to multilayer perceptrons (deep neural networks)", "description": "In this tutorial, we discuss how multilayer perceptrons, a standard neural-network architecture, can be employed for regression tasks. Specifically, we will use the ElemNet neural-network architecture to predict the volume per atom of inorganic compounds, where the Open Quantum Materials Database (OQMD) is used as a resource.", "date": "2021-01-29", - "category": "beginner_tutorial", + "category": "Beginner tutorial", "methods": [ { "name": "Supervised learning" @@ -22,7 +22,7 @@ "name": "Atomic features" } ], - "systems": [ + "applications": [ { "name": "Inorganic compounds" }, diff --git a/notebooks/perovskite-tolerance-factor.archive.json b/notebooks/perovskite-tolerance-factor.archive.json index 8ee23c7..367e75a 100644 --- a/notebooks/perovskite-tolerance-factor.archive.json +++ b/notebooks/perovskite-tolerance-factor.archive.json @@ -4,7 +4,7 @@ "name": "Finding a tolerance factor to predict perovskite stability with SISSO", "description": "This tutorial shows how a tolerance factor for predicting perovskite stability can be learned from data with the sure-independece-screening-and-sparsifying-operator (SISSO) descriptor-identification approach.", "date": "2022-05-18", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Supervised learning" @@ -31,7 +31,7 @@ "name": "Atomic features" } ], - "systems": [ + "applications": [ { "name": "Perovskites" } diff --git a/notebooks/query-nomad-archive.archive.json b/notebooks/query-nomad-archive.archive.json index 5cde39e..563142c 100644 --- a/notebooks/query-nomad-archive.archive.json +++ b/notebooks/query-nomad-archive.archive.json @@ -4,7 +4,7 @@ "name": "Querying the NOMAD 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.", "date": "2022-04-06", - "category": "query_tutorial", + "category": "Query tutorial", "methods": [ { "name": "Unsupervised learning" @@ -25,7 +25,7 @@ "name": "Random forest" } ], - "systems": [ + "applications": [ { "name": "Ternaries" } diff --git a/notebooks/sgd-alloys-oxygen-reduction-evolution.archive.json b/notebooks/sgd-alloys-oxygen-reduction-evolution.archive.json index e842906..b608e17 100644 --- a/notebooks/sgd-alloys-oxygen-reduction-evolution.archive.json +++ b/notebooks/sgd-alloys-oxygen-reduction-evolution.archive.json @@ -4,7 +4,7 @@ "name": "Introduction to subgroup discovery: Identifying outstanding transition-metal-alloy catalysts", "description": "This tutorial introduces, by means of two applications in materials science, the artificial-intelligence technique subgroup discovery.", "date": "2021-10-28", - "category": "intermediate_tutorial", + "category": "Intermediate tutorial", "methods": [ { "name": "Subgroup discovery" @@ -13,7 +13,7 @@ "name": "Decision tree" } ], - "systems": [ + "applications": [ { "name": "Heterogeneous catalysis" }, diff --git a/notebooks/sgd-propylene-oxidation-hte.archive.json b/notebooks/sgd-propylene-oxidation-hte.archive.json index eb3f533..ca79666 100644 --- a/notebooks/sgd-propylene-oxidation-hte.archive.json +++ b/notebooks/sgd-propylene-oxidation-hte.archive.json @@ -4,13 +4,13 @@ "name": "Learning Design Rules for Catalysts from High-Throughput Experimentation and Theory via Subgroup Discovery", "description": "This tutorial explores the application of subgroup discovery (SGD) to an experimental-theoretical data set in order to identify rules on key physicochemical parameters that describe the materials and environmental conditions associated with outstanding performance in heterogeneous catalysis.", "date": "2022-2-09", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Subgroup discovery" } ], - "systems": [ + "applications": [ { "name": "Heterogeneous catalysis" } diff --git a/notebooks/soap-atomic-charges.archive.json b/notebooks/soap-atomic-charges.archive.json index ba247e7..896662f 100644 --- a/notebooks/soap-atomic-charges.archive.json +++ b/notebooks/soap-atomic-charges.archive.json @@ -4,7 +4,7 @@ "name": "Machine 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 on atoms in small organic molecules.", "date": "2019-09-26", - "category": "intermediate_tutorial", + "category": "Intermediate tutorial", "methods": [ { "name": "Supervised learning" @@ -22,7 +22,7 @@ "name": "SOAP" } ], - "systems": [ + "applications": [ { "name": "GDB molecular database" }, diff --git a/notebooks/tcmi.archive.json b/notebooks/tcmi.archive.json index 9db412b..4c304cf 100644 --- a/notebooks/tcmi.archive.json +++ b/notebooks/tcmi.archive.json @@ -4,7 +4,7 @@ "name": "Introduction to total cumulative mutual information", "description": "This interactive notebook introduces the concepts and original implementation of total cumulative mutual information (TCMI), as presented in the related publication. The main results of the publication are also reproduced in a hands-on style", "date": "2020-02-06", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Supervised learning" @@ -31,7 +31,7 @@ "name": "TCMI" } ], - "systems": [ + "applications": [ { "name": "Synthetic data" }, diff --git a/notebooks/tetradymite-PRM2020.archive.json b/notebooks/tetradymite-PRM2020.archive.json index 21ba7fa..c1dcffc 100644 --- a/notebooks/tetradymite-PRM2020.archive.json +++ b/notebooks/tetradymite-PRM2020.archive.json @@ -4,7 +4,7 @@ "name": "Discovery of new topological insulators in alloyed tetradymites", "description": "Learn how to find descriptive parameters (short formulas) that predict whether alloyed materials are topological or trivial insulators, using the example of tetradymites. This notebook is based on the algorithm 'sure independence screening and sparsifying operator' (SISSO) that enables to search for optimal descriptor by scanning huge feature spaces.", "date": "2020-09-15", - "category": "advanced_tutorial", + "category": "Advanced tutorial", "methods": [ { "name": "Supervised learning" @@ -25,7 +25,7 @@ "name": "SISSO" } ], - "systems": [ + "applications": [ { "name": "Tetradymites" }, diff --git a/notebooks/tutorial_stats.ipynb b/notebooks/tutorial_stats.ipynb index 2a88021..8c146a0 100644 --- a/notebooks/tutorial_stats.ipynb +++ b/notebooks/tutorial_stats.ipynb @@ -176,7 +176,21 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "map_categories = {\n", + " 'advanced_tutorial': 'Advanced tutorial',\n", + " 'beginner_tutorial': 'Beginner tutorial',\n", + " 'intermediate_tutorial': 'Intermediate tutorial',\n", + " 'query_tutorial':'Query tutorial'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -187,9 +201,9 @@ " 'description': tutorial['description'],\n", " 'date': tutorial['updated'],\n", "\n", - " 'category': tutorial['labels']['category'][0],\n", - " 'methods': [ {'name': v } for v in tutorial['labels']['ai_methods'] ],\n", - " 'systems': [ {'name': v } for v in tutorial['labels']['application_system'] ],\n", + " 'category': map_categories[tutorial['labels']['category'][0]],\n", + " 'methods': [{'name': v} for v in tutorial['labels']['ai_methods']],\n", + " 'applications': [{'name': v} for v in tutorial['labels']['application_system']],\n", " 'platform': 'Python'\n", " }\n", "\n", @@ -301,7 +315,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.12.7" }, "orig_nbformat": 4 }, diff --git a/pyproject.toml b/pyproject.toml index 6cb61a2..8d13133 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ Repository = "https://github.com/FAIRmat-NFDI/nomad-aitoolkit" [project.optional-dependencies] dev = ["ruff", "pytest", "structlog"] -docs = ["mkdocs", "mkdocs-material==8.1.1", "pymdown-extensions", "mkdocs-click"] +docs = ["mkdocs", "mkdocs-material", "pymdown-extensions", "mkdocs-click"] [project.entry-points.'nomad.plugin'] aitookitschema = "nomad_aitoolkit:aitoolkit" diff --git a/src/nomad_aitoolkit/apps/__init__.py b/src/nomad_aitoolkit/apps/__init__.py index f21adc9..230b024 100644 --- a/src/nomad_aitoolkit/apps/__init__.py +++ b/src/nomad_aitoolkit/apps/__init__.py @@ -109,8 +109,8 @@ ), WidgetTerms( type='terms', - quantity=f'data.systems.name#{schema_name}', - title='Systems', + quantity=f'data.applications.name#{schema_name}', + title='Applications', scale=ScaleEnum.POW1, layout={ BreakpointEnum.XXL: Layout(h=6, w=6, x=12, y=0), diff --git a/src/nomad_aitoolkit/schema/__init__.py b/src/nomad_aitoolkit/schema/__init__.py index 44b6d7e..bcc842b 100644 --- a/src/nomad_aitoolkit/schema/__init__.py +++ b/src/nomad_aitoolkit/schema/__init__.py @@ -44,13 +44,13 @@ class Method(ArchiveSection): ) -class System(ArchiveSection): +class Application(ArchiveSection): m_def = Section(a_eln=ELNAnnotation(overview=True)) name = Quantity( type=str, a_eln=ELNAnnotation(component=ELNComponentEnum.StringEditQuantity), - description='Specifying name of the system.', + description='Specifying name of the application.', ) @@ -154,11 +154,10 @@ class AIToolkitNotebook(Schema): component=ELNComponentEnum.EnumEditQuantity, props=dict( suggestions=[ - 'advanced tutorial', - 'beginner tutorial', - 'intermediate tutorial', - 'query tutorial', - 'thermal transport', + 'Advanced tutorial', + 'Beginner tutorial', + 'Intermediate tutorial', + 'Query tutorial', ] ), ), @@ -175,7 +174,7 @@ class AIToolkitNotebook(Schema): methods = SubSection(section=Method, repeats=True) - systems = SubSection(section=System, repeats=True) + applications = SubSection(section=Application, repeats=True) references = SubSection(section=Reference, repeats=True)