34 citations · 52 across the 14 of their papers we have counts for
14 papers
Crystal-structure design by agentic AI in a language of motifs
Dinh-Khiet Le, Minh-Quyet Ha, Hong-Phuc Vu-Dinh +6
Data-driven materials discovery interpolates more reliably than it extrapolates and seldom reveals new structure types. We present MatEvolve, an agentic-AI framework that designs c…
Superconductor discovery in the emerging paradigm of Materials Informatics
Huan Tran, Hieu-Chi Dam, Christopher Kuenneth +2
The last two decades have witnessed a tremendous number of computational predictions of hydride-based (phonon-mediated) superconductors, mostly at extremely high pressures, i.e., h…
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…
Categorical data clustering: 25 years beyond K-modes
Tai Dinh, Wong Hauchi, Philippe Fournier-Viger +4
The clustering of categorical data is a common and important task in computer science, offering profound implications across a spectrum of applications. Unlike purely numerical dat…
Function Decomposition Tree with Causality-First Perspective and Systematic Description of Problems in Materials Informatics
Hiori Kino, Hieu-Chi Dam, Takashi Miyake +1
As interdisciplinary science is flourishing because of materials informatics and additional factors; a systematic way is required for expressing knowledge and facilitating communic…
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…
Variational Hyper-Encoding Networks
Phuoc Nguyen, Truyen Tran, Sunil Gupta +3
We propose a framework called HyperVAE for encoding distributions of distributions. When a target distribution is modeled by a VAE, its neural network parameters θis drawn from a d…
Measuring the Similarity between Materials with an Emphasis on the Materials Distinctiveness
Tran-Thai Dang, Tien-Lam Pham, Hiori Kino +2
In this study, we establish a basis for selecting similarity measures when applying machine learning techniques to solve materials science problems. This selection is considered wi…
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…
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…
Machine learning reveals orbital interaction in crystalline materials
Tien Lam Pham, Hiori Kino, Kiyoyuki Terakura +4
We propose a novel representation of crystalline materials named orbital-field matrix (OFM) based on the distribution of valence shell electrons. We demonstrate that this new repre…
A regression-based feature selection study of the Curie temperature of transition-metal rare-earth compounds: prediction and understanding
Hieu Chi Dam, Viet Cuong Nguyen, Tien Lam Pham +4
The Curie temperature () of binary alloy compounds consisting of 3 transition-metal and 4 rare-earth elements is analyzed by a machine learning technique. We first demon…