https://github.com/aronwalsh/MLforMaterials

Online resource for a practical course in machine learning for materials research at Imperial College London (MATE70026)
https://github.com/aronwalsh/MLforMaterials

machine-learning materials-chemistry materials-informatics materials-science

Last synced: 5 days ago
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Online resource for a practical course in machine learning for materials research at Imperial College London (MATE70026)

• Host: GitHub
• URL: https://github.com/aronwalsh/MLforMaterials
• Owner: aronwalsh
• License: cc0-1.0
• Created: 2023-08-02T14:29:12.000Z (over 1 year ago)
• Default Branch: 2025
• Last Pushed: 2025-02-14T07:48:51.000Z (2 months ago)
• Last Synced: 2025-02-14T08:32:46.172Z (2 months ago)
• Topics: machine-learning, materials-chemistry, materials-informatics, materials-science
• Language: Jupyter Notebook
• Homepage: https://aronwalsh.github.io/MLforMaterials
• Size: 84.3 MB
• Stars: 70
• Watchers: 2
• Forks: 11
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• best-of-atomistic-machine-learning - GitHub

README

        
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# Machine Learning for Materials

Online resource of a practical machine learning course in the Department of Materials at Imperial College London.

You have the option to browse the files or download the complete folder using the green `clone or download` button on the top right of the screen ([zip file](https://github.com/aronwalsh/MLforMaterials/archive/master.zip)).

## Course Description

_Machine Learning for Materials_ (MATE70026) provides an introduction to statistical research tools for materials theory and simulation. It is aimed at senior undergraduate or junior postgraduate students. 

You will consider how composition-structure-property information in materials science can be represented in a form suitable for machine learning. You will then build, train, and evaluate your own models using public tools and open datasets. 

A hybrid teaching style will be followed with a mixture of lectures and assignments. The course assumes a basic working knowledge of the Python 3 programming language.

[Lecture Slides](./slides)

[Post a Query](https://github.com/aronwalsh/MLforMaterials/issues)

## Course Website

You can view the site at [https://aronwalsh.github.io/MLforMaterials](https://aronwalsh.github.io/MLforMaterials)

To build a local copy, first install [Jupyter Book](https://jupyterbook.org):

`pip install -U jupyter-book`

then enter the repository and run 

`jupyter-book build .`

## Acknowledgements

This module was developed by Aron Walsh with the assistance of [Anthony Onwuli](https://github.com/AntObi) and [Zhenzhu Li](https://github.com/lizhenzhupearl).