A machine learning approach to predict the structural and magnetic properties of Heusler alloy families

TL;DR

This paper employs a random forest regression model to accurately predict structural and magnetic properties of various Heusler alloys, demonstrating robustness and high correlation with DFT calculations and experimental data.

Contribution

It introduces a machine learning approach using RF regression to predict properties of Heusler alloys, showing high accuracy and robustness over traditional methods.

Findings

RF model achieves R2 values between 0.80 and 0.94.

Predicted properties closely match DFT and experimental results.

Valence electron number is a key feature in predictions.

Abstract

Random forest (RF) regression model is used to predict the lattice constant, magnetic moment and formation energies of full Heusler alloys, half Heusler alloys, inverse Heusler alloys and quaternary Heusler alloys based on existing as well as indigenously prepared databases. Prior analysis was carried out to check the distribution of the data points of the response variables and found that in most of the cases, the data is not normally distributed. The outcome of the RF model performance is sufficiently accurate to predict the response variables on the test data and also shows its robustness against overfitting, outliers, multicollinearity and distribution of data points. The parity plots between the machine learning predicted values against the computed values using density functional theory (DFT) shows linear behavior with adjusted R2 values lying in the range of 0.80 to 0.94 for all the predicted properties for different types of Heusler alloys. Feature importance analysis shows that the valence electron numbers plays an important feature role in the prediction for most of the predicted outcomes. Case studies with one full Heusler alloy and one quaternary Heusler alloy were also mentioned comparing the machine learning predicted results with our earlier theoretical calculated values and experimentally measured results, suggesting high accuracy of the model predicted results.