# Training of AI Surrogate Models

This notebook contains the current experiments for training AI models that attempt to predict the chemistry component of POET.

The repository is structured as follows:
```
└── dataset
    └── Barite_50_Data_training.h5
    └── barite_50_4_corner.h5
└── doc
└── results
└── src
    └── POET_Training.ipynb
    └── convert_data.jl
    └── optuna_runs.py
    └── preprocessing.py
```

The datasets in `datasets` must first be pulled via `git lfs pull` to get the data from the large file storage.
A conda environment can then be set up with the packages contained in environment.yml with `conda env create -f environment.yml`

The `preprocessing.py` file defines all the necessary steps for preprocessing as well as the keras models used. The actual training and additional explanations then take place in `POET_Training.ipynb`.