5 papers
Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery
Minh-Quyet Ha, Dinh-Khiet Le, Duc-Anh Dao +6
Discovering novel high-entropy alloys (HEAs) with desirable properties is challenging due to the vast compositional space and complex phase formation mechanisms. Efficient explorat…
Ensemble learning reveals dissimilarity between rare-earth transition metal binary alloys with respect to the Curie temperature
Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen +3
We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kern…
Explainable Machine Learning for Materials Discovery: Predicting the Potentially Formable Nd-Fe-B Crystal Structures and Extracting Structure-Stability Relationship
Tien-Lam Pham, Duong-Nguyen Nguyen, Minh-Quyet Ha +3
New Nd-Fe-B crystal structures can be formed via the elemental substitution of LATX host structures, including lanthanides LA, transition metals T, and light elements X as B, C, N,…
Boron cage effects on Nd-Fe-B crystal structure's stability
Duong-Nguyen Nguyen, Duc-Anh Dao, Takashi Miyake +1
In this study, we investigate the structure-stability relationship of hypothetical Nd-Fe-B crystal structures using descriptor-relevance analysis and the t-SNE dimensionality reduc…
Committee machine that votes for similarity between materials
Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen +4
We developed a method for measuring the similarity between materials, focusing on specific physical properties. The obtained information can be utilized to understand the underlyin…