Commit cefda0cb authored by Luigi's avatar Luigi
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# 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 ``, this tutorial requires [`qmmlpack`]( on the DEVELOPMENT branch.
It also requires the environment variable `CML_PLUGINS=cscribe` to be set.
## Todos/Future plans
- Once `cmlkit` supports caching, expand the result analysis section
- Add a bandgap appendix (if feasible)
- Potentially add another representation (ideally a global one)
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"authors": [
"Langer, Marcel"
"email": "",
"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": "",
"link": "",
"link_public": "",
"updated": "2020-05-22",
"featured": false,
"top_of_list": false
"labels": {
"application_keyword": [
"Formation energy prediction"
"application_section": [
"Tutorials for artificial-intelligence methods"
"application_system": [
"Group-III oxides"
"category": [
"data_analytics_method": [
"Kernel ridge regression",
"platform": [
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import json
from setuptools import setup, find_packages
with open('metainfo.json') as file:
metainfo = json.load(file)
author=', '.join(metainfo['authors']),
install_requires=['seaborn', 'cmlkit', 'cscribe'],
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