5 papers · 1 filter
Learning Hidden Chemistry with Deep Neural Networks
Tien-Cuong Nguyen, Van-Quyen Nguyen, Van-Linh Ngo +2
We demonstrate a machine learning approach designed to extract hidden chemistry/physics to facilitate new materials discovery. In particular, we propose a novel method for learning…
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,…
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…
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…