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MLatom tutorial for Machine Learning for Chemistry Summer School

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# MLatom tutorials for the CZS Summer School

This is the collection of the [MLatom](https://github.com/dralgroup/mlatom) tutorials for the [CZS Summer school 2024](https://aimat.iti.kit.edu/ml4chem2024.php) in Karlsruhe. MLatom is a general purpose software platform for AI-enhanced computational chemistry. MLatom has an extensive documentation with many other useful tutorials: https://xacs.xmu.edu.cn/docs/mlatom/.

## Lecture notes
Pavlo O. Dral's [lecture notes](http://dr-dral.com/wp-content/uploads/2024/09/CZS_Dral_wm.pdf) related to the tutorials.

## How to run the tutorials
- not recommended but if you insist: you can run the notebooks on [Google colab](https://drive.google.com/drive/folders/18KMn1LW5pXiqC18MX6OBGE9Fyy0DMbZF?usp=sharing).
- strongly recommended to run the tutorials on the XACS cloud where everything is installed: http://XACScloud.com (registration and basic use is free). After the registration, we will give you access to the Jupyter Lab. The Jupyter notebooks will be available in your home folder: ``jupyter_examples/karlsruhe2024``. Click on 'Jupyter Lab' on the left menu bar, then on 'Launch Server', and finally on 'Connect'.
- you can also get support on XACS Slack workspace: https://join.slack.com/t/xacs-support/shared_invite/zt-1gm1lpn68-pReQhfYGu813eCqwmvdGvA (``#karlsruhe2024`` channel)

## Tutorials

Overview of tutorial Jupyter notebooks with links to Google colab versions (feel free to do them in any order):

1. Basic introduction to MLPs [`MLP_intro.ipynb`](https://drive.google.com/file/d/1tPMbsygKIjS0EdkFMBry2r7yYrVXSwg0/view?usp=sharing)
2. Testing MLPs [`test_mlp.ipynb`](https://drive.google.com/file/d/1GUWMFdgFS9DKs5BMPGFAkPlMzD3HU6gD/view?usp=sharing)
3. Active learning for MLPs [`al.ipynb`](https://drive.google.com/file/d/1yfTHZQ8BlwMg6fPUIm1TSrTvAWuuOyBm/view?usp=sharing)
4. Universal MLP: strengths and pitfalls [`universal_mlp.ipynb`](https://drive.google.com/file/d/1yf4wMPRCODdW02jw2y_MtW_TtKmObNBx/view?usp=sharing)
5. Transfer learning [`tl.ipynb`](https://drive.google.com/file/d/165KZj5g77czczRXi2KPD7NUqXlVCSKH2/view?usp=sharing)
6. Delta-learning [`delta.ipynb`](https://drive.google.com/file/d/1i-Vw0W7nwpDA97TZalk4KtJy6VP3aZMH/view?usp=sharing)
7. Multi-state MLP for excited states and surface-hopping dynamics [`msani.ipynb`](https://drive.google.com/file/d/11FxqeXfcwcOj_dcucd_k91XCuZzU0dh2/view?usp=sharing)

## Contributors

These tutorials are the result of the contributions by:

- [Pavlo O. Dral](http://dr-dral.com)
- Fuchun Ge
- Yifan Hou
- Mikolaj Martyka
- [Jakub Martinka](https://jakubmartinka.github.io/)

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