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ABSTRACT
This research project delves into the characterization of the bulk modulus properties of the TiCoSb half-Heusler alloy through a combined approach involving Density Functional Theory (DFT) and Supervised Machine Learning (SML) techniques. TiCoSb, a member of the Half-Heusler compound family, shows promise as a mid-to high-temperature thermoelectric material due to its desirable characteristics such as non-toxicity, excellent electrical properties, thermal stability, and mechanical robustness. The primary challenge in utilizing TiCoSb for thermoelectric applications lies in its relatively high thermal conductivity, which hinders its efficiency. To address this issue, strategies like substituting isoelectronic elements in the Ti-site lattice have been explored to reduce thermal conductivity while maintaining a high power factor. In this study, we provide two methodologies, namely first principle calculations, to analyse the bulk modulus of TiCoSb. The obtained value of around 126.1 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 High pressure studies and thermal conductivity. 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.
