/Active-Learning-HESOs

Active learning for High-Entropy-Spinel-Oxides Discovery

Primary LanguageJupyter NotebookMIT LicenseMIT

Active-Learning-HESOs

Active learning for High-Entropy-Spinel-Oxides Discovery

Database

Database Creation, data import and export.

  1. Create the database.
    python create.py
    
  2. Import experimental data.
    python import.py --input database/raw_data/purity_KS.csv --property purity --tag KS
    python import.py --input database/raw_data/purity_al_init.csv --property purity
    python import.py --input database/raw_data/T90_al_init.csv --property T90
    python import.py --input database/raw_data/al1.csv --property purity
    python import.py --input database/raw_data/al1.csv --property T90
    python import.py --input database/raw_data/al2.csv --property purity
    python import.py --input database/raw_data/al2.csv --property T90
    python import.py --input database/raw_data/al3.csv --property purity
    python import.py --input database/raw_data/al3.csv --property T90
    python import.py --input database/raw_data/al4.csv --property purity
    python import.py --input database/raw_data/al4.csv --property T90
    
  3. Optionally, you can export experimental data.
    python export.py --property purity
    
  4. Active Learning.
    python3 active_learning.py --n_samples 5
    

Machine Learning

See notebook.