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In this study, we attempted to model vacancy ordered half Heusler compoundswith 18 valence electron count (VHH) derived from 19 VEC compounds such asTiNiSb such that the compositions will be Ti0.75NiSb, Zr0.75NiSb and Hf0.75NiSbwith semiconducting behavior. The main motivation is that such avacancy-ordered phase not only introduces semi conductivity but also itdisrupts the phonon conducting path in HH alloys and thus reduces the thermalconductivity and as a consequence enhances the thermoelectric figure of merit.In order to predict the formation energy ({\Delta}Hf) from composition andcrystal structure we have used 4684 compounds for their {\Delta}Hf values areavailable in the material project database and trained a machine learning modelwith R2 value of 0.943. Using this trained model, we have predicted the{\Delta}Hf of a list of VHH. From the predicted database of VHH we haveselected Zr0.75NiSb and Hf0.75NiSb to validate the machine learning predictionusing accurate DFT calculation. The calculated {\Delta}Hf for these twocompounds from DFT calculation are found to be comparable with our MLprediction. The calculated electronic and lattice dynamics properties show thatthese materials are narrow band gap semiconductors and are dynamically stableas their all-phonon dispersion curves are having positive frequencies. Thecalculated Seebeck coefficient, electrical conductivity as well as thermalconductivity, power factor and thermoelectric figure of merit are analyzed.

Accelerating Discovery of Vacancy Ordered 18-Valence Electron  Half-Heusler Compounds: A Synergistic Approach of Machine Learning and  Density Functional Theory