4 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…
Pairwise interactions for Potential energy surfaces and Atomic forces with Deep Neural network
Van-Quyen Nguyen, Viet-Cuong Nguyen, Tien-Cuong Nguyen +1
Molecular dynamics (MD) simulation, which is considered an important tool for studying physical and chemical processes at the atomic scale, requires accurate calculations of energi…
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…
Important descriptors and descriptor groups of Curie temperatures of rare-earth transition-metal binary alloys
Hieu Chi Dam, Viet Cuong Nguyen, Tien Lam Pham +4
We analyze Curie temperatures of rare-earth transition metal binary alloys with machine learning method. In order to select important descriptors and descriptor groups, we introduc…