DEVELOPING A MACHINE LEARNING MODEL UTILIZING THE SUPPORT VECTOR MACHINES (SVM) TECHNIQUE TO PREDICT THE BULK MODULUS PROPERTIES OF TaFeSb HUESLER ALLOY AND COMPARING THE RESULTS WITH DFT FIRST PRINCIPLE CALCULATION

ABSTRACT

Tantalum iron antimonide (TaFeSb), a half-Heusler alloy, demonstrates the ability to alter its conductivity characteristics based on variations in temperature or composition. Previous studies have provided evidence to support the assertion that TaFeSb exhibits several desirable characteristics, notably a significantly elevated bulk modulus. This characteristic renders it a potential candidate for various applications, encompassing the fields of structural engineering and thermoelectricity. In this study, we provide two methodologies, namely first principle calculations, to analyze the bulk modulus of TaFeSb. The obtained value of around 145 GPA for the bulk modulus indicates the material’s robustness and stiffness. The aforementioned feature assumes a pivotal role in its various applications within the fields of spintronics and thermoelectricity. In this study, a machine learning (ML) model is employed to forecast the bulk modulus. A total of 2382 ternary compounds with elastic data were acquired from the Aflow datasets through the utilization of the Application Program Interface (API). The estimation of the Root Mean Squared Error (RMSE) was associated with a Standard Error of 0.0135. Additionally, the Uncertainty Calibration was measured at 0.662, indicating a calibration uncertainty percentage error of 2.6. The models that have been built exhibit a significant level of promise in terms of their accuracy, precision, and ease of implementation, which enhances their practical usefulness.