ABSTRACT
Vanadium iron antimonite (VFeSb), a half-Heusler alloy, demonstrates the ability to alter its con
ductivity characteristics based on variations in temperature or composition. Previous studies have
provided evidence to support the assertion that VFeSb 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 VFeSb. 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 mechanical 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 17.09. Additionally, the Uncertainty Calibration was measured at 0.919, 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.
