194 citations · 1.3k across the 32 of their papers we have counts for
32 papers
Crystal-structure design by agentic AI in a language of motifs
Dinh-Khiet Le, Minh-Quyet Ha, Hong-Phuc Vu-Dinh +3
Data-driven materials discovery interpolates more reliably than it extrapolates and seldom reaches new structure types. We present MatEvolve, an agentic-AI framework designing crys…
Omics-scale polymer computational database transferable to real-world artificial intelligence applications
Ryo Yoshida, Yoshihiro Hayashi, Hidemine Furuya +103
Developing large-scale foundational datasets is a critical milestone in advancing artificial intelligence (AI)-driven scientific innovation. However, unlike AI-mature fields such a…
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…
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…