Machine Learning Based Classification of Non-functionalized MXenes into Metals and Non-metals

Introduction

This project deals with training classification models on physical properties of non-functionalized MXene molecules to classify them into metals and non-metals. The data required was obtained from aNANt database by IISc, Bangalore.

Data Visualization & Preprocessing

List of Features

Distribution of individual features

Corelation Heatmap

Feature Engineering

For each of the 5 atoms in the molecule we add the following properties to the molecule’s data:

1. Neutron number
2. Proton number
3. Number of electrons
4. Period
5. Group number
6. Atomic radius
7. Electronegativity
8. First Ionization Energy
9. Density
10. Melting point
11. Boiling point
12. Number of isotopes
13. Number of shells
14. Specific heat
15. Mass Magnetic Susceptibility
16. Molar Magnetic Susceptibility
17. Thermal Conductivity
    So, now, in addition to the 29 features we had earlier, we now have 85 new features (17 properties x 5 atoms per molecule). Therefore, a total of 131 features.

Distribution of individual features (After adding new features)

Correlation Heatmap

Class Imbalance

Results

Model : Random Forest (On oversampled data)

ROC Curve

ROC-AUC = 0.949

Accuracy = 93.2%

Model : Neural Network (Cost-sensitive)

ROC Curve

ROC-AUC = 0.962

Accuracy = 93.67%