## 4 Conclusions and Future Directions  
In this study, ML methods using the HEA interaction network were improved for phase prediction. A method presented in this study predicted the phase using the KNN algorithm based on a graph. The proposed approach was compared with six other methods, and the accuracy (88.88%) of the proposed approach was considerably higher than that of the others. The phase prediction with the proposed method was about 11% and 19% more accurate than the Generalized Linear model and Random Forest model, respectively. The HEA compound design and feature selection has potential scope. The compound phase expresses the state of matter that is affected in determining their application. The conclusions are explained as follows: (i) the proposed approach employed the HEA interaction network for phase prediction; (ii) neighbors with strong connections have more similarity to the target compound for prediction; (iii) the learning performance of the proposed approach is comparable to other ML methods; (iv) finally, phases were predicted and validated. The proposed approach is scalable on different datasets with accuracy preservation. Our future work will focus principally on raising the efficiency of the ML methods for HEA phase prediction and connection prediction. Conceptualization, R.G.N.N.N., M.J. and M.H. ; methodology, R.G.N.N.N., M.J. and M.H. ; software, R.G.N.N.N., M.J. and M.H. ; validation, R.G.N.N.N., M.J. and M.H. ; formal analysis, R.G.N.N.N., M.J. and M.H. ; investigation, R.G.N.N.N., M.J. and M.H. ; resources, R.G.N.N.N., M.J. and M.H. ; data curation, R.G.N.N.N., M.J. and M.H. ; writing-original draft preparation, R.G.N.N.N., M.J. and M.H. ; writing--review and editing, R.G.N.N.N., M.J. and M.H. ; visualization, R.G.N.N.N., M.J. and M.H. ; supervision, M.J.; project administration, M.J.; funding acquisition, M.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Institutional Review Board Statement: Institutional review board is not applicable to this study. Informed Consent Statement: Information consent was obtained from all subjects involved in the study. Data Availability Statement: Data availability is contained within the article. Acknowledgments: The authors acknowledge funding by Federal Ministry of Education and Research of Germany for the project STREAM (\"Semantische Reprisentation, Vermetzung und Kuratierung von qualitit sgesicherten Materialdaten\", ID: 16 QK11C). Furthermore, the authors acknowledge support by the KIT-Publication Fund of the Karlsruhe Institute of Technology. Conflicts of Interest: The authors declare no conflict of interest. References  
* [1] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] High-entropy alloy: Challenges and prospects. _Mater. Today_**2016**, _19_, 349-362. [CrossRef]
* [2] [PERSON]; [PERSON]; [PERSON]; [PERSON] Improving phase prediction accuracy for high entropy alloys with machine learning. _Comput. Mater. Sci._**2021**, _192_, 110389. [CrossRef]
* [3] [PERSON]; [PERSON] Phase stability in high entropy alloys: Formation of solid-solution phase or amorphous phase. _Prog. Nat. Sci. Mater. Int._**2011**, _21_, 433-446. * [4] [PERSON]; [PERSON]; [PERSON]; [PERSON] Prediction of the composition and hardness of high-entropy alloys by machine learning. _JOM_**2019**, _71_, 3433-3442. [CrossRef]
* [5] [PERSON]; [PERSON]; [PERSON] A tool to predict the evolution of phase and Young's modulus in high entropy alloys using artificial neural network. _Comput. Mater. Sci._**2021**, _197_, 110619. [CrossRef]
* [6] [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-label phase-prediction in high-entropy-alloys using Artificial-Neural-Network. _Mater. Lett._**2020**, _268_, 127606. [CrossRef]
* [7] [PERSON] Machine learning-based prediction of phases in high-entropy alloys. _Comput. Mater. Sci._**2021**, _188_, 110244.