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Exploring the vast compositional space offered by multicomponent systems or high entropy materials using the traditional route of materials discovery, one experiment at a time, is prohibitive in terms of cost and required time. Consequently, the development of high‐throughput experimental methods, aided by machine learning and theoretical predictions will facilitate the search for multicomponent materials in their compositional variety. In this study, high entropy oxides are fabricated and characterized using automated high‐throughput techniques. For intuitive visualization, a graphical phase–property diagram correlating the crystal structure, the chemical composition, and the band gap are introduced. Interpretable machine learning models are trained for automated data analysis and to speed up data comprehension. The establishment of materials libraries of multicomponent systems correlated with their properties (as in the present work), together with machine learning‐based data analysis and theoretical approaches are opening pathways toward virtual development of novel materials for both functional and structural applications.

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Technique Coating Reference Balanced magnetron Nano-composites of NC-TiC wear applications (Pavel, et al., 2011) Balanced magnetron Optical properties of AlSiN nano-composites (Liu, et al., 2009) Balanced magnetron Hard coatings to decorative applications (Raymond & Baham, 1999) Balanced magnetron Nd-Fe-B Film for magnetic applications (Liu, et al., 20007) Balanced magnetron Optical applications. (Stefan, et al., 2008) Unbalanced magnetron Hard films for corrosion and wear applications (Rodil & Olaya, 2006). Unbalanced magnetron Nb Films for biological applications (Olivares, et al.,2011) Unbalanced magnetron NbO films for biological applications (Ramirez, et al., 2011) Unbalanced magnetron Electrical applications of NbN films. (Olaya, et al., 2008,) Unbalanced magnetron Diamond-like carbon films for infrared transmission enhancement (Lingxia & Xu, 2000).

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