Throughout the years, parametric and computational approaches have been used extensively for the design of High Entropy Alloys (HEA) and Multicomponent Element Alloys (MEA). Machine learning (ML) approaches have been extensively used to circumvent the fundamental issues that challenge the proposed theories, models, and methods for conventional alloys. Highly accurate ML models rely heavily on the quality of data and the design features that are used as inputs to the model. The applicability of these methods for phase formation predictions is questionable when it comes to the design of thermally sprayed HEA coatings using gas or water atomized powders as feedstock material. Phase formation from liquid state depends on the cooling rate during solidification which is several orders of magnitude higher when compared to arc melted as-cast HEAs. In addition, during plasma spray the powder melts in the flame and re-solidifies under different cooling rates during deposition. To our knowledge, almost all ML algorithms are based on available datasets constructed from relatively low cooling rate processes such as arc melting and suction casting. A new approach is needed to broaden the applicability of ML algorithms to rapid solidification manufacturing processes similar to gas and water atomization by making use of existing data and theoretical models. In this study the authors introduce a cooling rate dependent design feature that can lead to accurate predictions of the HEA powder phase formation and the subsequent phases found in the spray coated materials. The model is validated by comparing the predictions with existing coating related data in the literature.