A Graph-Based k-Nearest Neighbor (KNN) Approach for Predicting Phases in High-Entropy Alloys
Abstract
:1. Introduction
2. Materials and Methods
2.1. Theoretical Notation Definitions
- Social network graph. A social network can be mapped to the graph , where is the node set and is the edge set.
- Neighbors. A node is a neighbor of node in graph if there is an edge .
- HEA interaction network. The HEA compounds are nodes, and the interaction between two compounds are edges that are mapped into a social network [19].
- Target compound. The node considered for phase prediction in the HEA interaction network is called the target compound.
- Voting. The HEA compound is classified by a plurality vote of its neighbors in the KNN algorithm, with the HEA compound being assigned to the phase most common among its k-nearest neighbor.
2.2. Proposed Method
2.3. Evaluation
3. Results and Discussion
4. Conclusions and Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Ghouchan Nezhad Noor Nia, R.; Jalali, M.; Houshmand, M. A Graph-Based k-Nearest Neighbor (KNN) Approach for Predicting Phases in High-Entropy Alloys. Appl. Sci. 2022, 12, 8021. https://doi.org/10.3390/app12168021
Ghouchan Nezhad Noor Nia R, Jalali M, Houshmand M. A Graph-Based k-Nearest Neighbor (KNN) Approach for Predicting Phases in High-Entropy Alloys. Applied Sciences. 2022; 12(16):8021. https://doi.org/10.3390/app12168021
Chicago/Turabian StyleGhouchan Nezhad Noor Nia, Raheleh, Mehrdad Jalali, and Mahboobeh Houshmand. 2022. "A Graph-Based k-Nearest Neighbor (KNN) Approach for Predicting Phases in High-Entropy Alloys" Applied Sciences 12, no. 16: 8021. https://doi.org/10.3390/app12168021