Init

.gitignore

+.envrc
+.ipynb_checkpoints
+run_*/
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README.md

+# Kernel Ridge Regression for Materials
+
+Tutorial on kernel ridge regression for the block course "Big data and artificial intelligence in materials science" as part of the Max Planck Graduate Center for Quantum Materials.
+
+Topics covered:
+- Kernel ridge regression
+	- Fundamentals
+	- HP Optimisation
+	- Toy implementation
+- Representations
+- Application to the NOMAD2018 challenge dataset
+	- Strategy
+	- Dataset exploration
+	- HP Optimisation
+	- Discussion of results
+
+## Technicalities
+
+In addition to the prerequisites in `setup.py`, this tutorial requires [`qmmlpack`](https://gitlab.com/qmml/qmmlpack/-/tree/development) on the DEVELOPMENT branch.
+
+It also requires the environment variable `CML_PLUGINS=cscribe` to be set.

metainfo.json

+{
+  "authors": [
+    "Langer, Marcel"
+  ],
+  "email": "langer@fhi-berlin.mpg.de",
+  "title": "Kernel Ridge Regression for Materials Property Prediction: A Tutorial Introduction",
+  "description": "In this tutorial, we'll 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.",
+  "url": "https://gitlab.mpcdf.mpg.de/nomad-lab/analytics-tutorial-krr4mat",
+  "link": "https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/Welcome.ipynb",
+  "link_public": "https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/Welcome.ipynb",
+  "updated": "2020-05-22",
+  "flags":{
+    "featured": false,
+    "top_of_list": false
+  },
+   "labels": {
+    "application_keyword": [
+      "Formation energy prediction"
+    ],
+    "application_section": [
+      "Materials property prediction"
+    ],
+    "application_system": [
+      "Group-III oxides"
+    ],
+    "category": [
+      "Tutorial"
+    ],
+    "data_analytics_method": [
+      "Kernel ridge regression",
+      "SOAP",
+    ],
+    "platform": [
+      "jupyter"
+    ]
+
+  }
+}
+}
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setup.py

+import json
+from setuptools import setup, find_packages
+
+with open('metainfo.json') as file:
+    metainfo = json.load(file)
+
+setup(
+    name='krr4mat',
+    version='0.1',
+    author=', '.join(metainfo['authors']),
+    author_email=metainfo['email'],
+    url=metainfo['url'],
+    description=metainfo['title'],
+    long_description=metainfo['description'],
+    packages=find_packages(),
+    install_requires=['seaborn', 'cmlkit', 'cscribe'],
+)