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Optimizing Material-Designs with ML

The code in this repository accompanies the paper “Exploring Design Space: Machine Learning for Multi-Objective Materials Design Optimization with Enhanced Evaluation Strategies“.

Getting Started

A basic Linux machine with an installation of Anaconda is able to run the code. With the command:

python train.py -m <Model>

The training for the defined model starts for all datasets in the folder data/run, with the configurations set in the config.py file.

With the command:

python opti_sim_pan.py

and

python opti_sim_trc.py

The optimization for the respective use case with the in the script defined model and optimizer, with the configurations set in the config.py file. The model.pkl saved in the folder ml_models_bin/Use-Case/Model-name is used as the model. The saved model.pkl used in the study are originally stored in the corresponding folders.

We recommend starting the script from a tmux session.

Console Output

Model Training

vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Dataset name: dataset-name, type: (full/split)

---------------------------------------
Task name: 'dataset-name'_'task-name'
train_size for outer loop = X_train-size
---------- SPLIT 1 ---------- (outer split)
*Starting Time*
Training over X_training-time min started

Results from this outer split

---------- SPLIT 2 ---------- (outer split)
*Starting Time*
Training over X_training-time min started

Results from this outer split

[...all outer splits...]


^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Next Task

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv

Next Dataset

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Saved Results

Model Training

All results are saved in the directory workdir/models in the following folder: dataset-name\y_label\start-time.
In this folder is saved the file regression_summary.csv, in which all performance metrics for each fold are stored.
In the folders split_'x' the outputs of the AutoML frameworks are stored, which are framework-specific.

Optimization

All results are saved in the directory workdir/optimization in the following folder: dataset-name\model\optimizer-name\opt-task_start-time. In this folder are saved the files:

  • results.txt: in which all results of the optimization are stored
  • optimization_result.pkl: in which all optimization steps are stored
  • optimization_result.pkl: in which all configurations and final results of the optimization are stored

Plot Results

The code for visualizing the results and reproducing the figures from the paper can be found in the following scripts:

  • evaluate_opti_pan.ipynb
  • evaluate_opti_trc.ipynb
  • plot_sim_trc_f_over_dis.ipynb
  • plot_splitting_strategies.ipynb

Datasets and License

The datasets used in the study are stored in the folder data/storage