HECC phase prediction

Machine learning models for predicting the single-phase synthesizability of high-entropy-ceramic carbides (HECCs)

Note:

If you found strange errors for running scripts under this repo on Windows, please try again on Linux instead.

1. Quick prediction from chemical formulas

1.1 Print help tab

python predict_from_formula.py -h

Any uninstalled modules can be installed by pip or anaconda. Click here for more details.

Output:

HECC phase prediction.

optional arguments:
  -h, --help            show this help message and exit
  --ann_model_path ANN_MODEL_PATH
                        Path to the ANN model.
  --svm_model_path SVM_MODEL_PATH
                        Path to the SVM model.
  --max_min_path MAX_MIN_PATH
                        Path to the file that contains the max and min values
                        of previous features.
  --formula FORMULA [FORMULA ...]
                        A list of chemical formula that contains the cations
                        only.

1.2 Predict single-phase synthesizability from chemical formulas

Example:
Run:

python predict_from_formula.py --formula TiVCrNbTa VCrNbMoTa TiVCrZrMo # Only cations should be included here.

Output:

Phase code: Single phase: 0.0; multi phase: 1.0

Prediction(s) from ANN: 0.049 0.047 1.000
Prediction(s) from SVM: 0.000 0.000 1.000

Note:

• These formulas give the same result: TiVCrNbTa, Ti1V1C1rNb1Ta1, Ti0.2V0.2Cr0.2Nb0.2Ta0.2, Ti0.03V0.03Cr0.03Nb0.03Ta0.03.

• Direct predictions in the output are the multi-phase probability, NOT the single-phase probability because single- and multi-phase samples were labeled as 0 and 1, respectively.

2. Source code of Artificial neural network

3. Source code of Support vector machine

4. Input features used in refined models

Feature	Description
ΔSmix	Mixing entropy
Vaverage	Average volume of constituent TMCs per formula unit
σV	Volume deviation of constituent TMCs
maverage	Average mass of constituent TMCs per formula unit
σm	Mass deviation of constituent TMCs
ρaverage	Average density of constituent TMCs
σρ	Density deviation of constituent TMCs
σχ	Deviation of electronegativity of constituent TMCs
VECaverage	Valence electron concentration (VEC) of HECC candidates
σVEC	VEC deviation of constituent TMCs

Data of all input features: click here

5. Dependent modules used in this project

• Python 3.8.5
• numpy==1.19.5
• pymatgen==2020.11.11
• tensorflow==2.4.1
• scikit-learn==0.24.1