6 papers · 1 filter
Complex crystal structure prediction using ML-enhanced multi-minima iterative genetic algorithm
Ling Tang, Weiyi Xia, Tyler J. Slade +2
Current machine learning (ML) approaches for materials discovery rely heavily on known structural databases, limiting their ability to identify entirely novel structure types. In t…
Physical properties of RCoAl (R = Gd-Tm, Y) single crystals
Sushma Kumari, Fernando A. Garcia, Juan Schmidt +6
Rare-earth (R) based intermetallic compounds can often exhibit diverse physical properties and distinct magnetic anisotropies. A Notable example are the light rare earth members of…
Use of frit-disc crucible sets to make solution growth more quantitative and versatile
Paul C. Canfield, Tyler J. Slade
The recent availability of step-edge, frit-disc crucible sets (generally sold as Canfield Crucible Sets or CCS) has led to multiple innovations associated with our group's use of s…
Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs
Weiyi Xia, Wei-Shen Tee, Paul C. Canfield +6
The discovery of novel quantum materials within ternary phase spaces containing antagonistic pair such as Fe with Bi, Pb, In, and Ag, presents significant challenges yet holds grea…
Search for stable and low-energy Ce-Co-Cu ternary compounds using machine learning
Weiyi Xia, Wei-Shen Tee, Paul Canfield +2
Cerium-based intermetallics have garnered significant research attention as potential new permanent magnets. In this study, we explore the compositional and structural landscape of…
LaCoX (X = Pb, Bi, Sb): a demonstration of antagonistic pairs as a route to quasi-low dimensional ternary compounds
Tyler J. Slade, Nao Furukawa, Matthew Dygert +6
We outline how pairs of strongly immiscible elements, referred to here as antagonistic pairs, can be used to synthesize ternary compounds with quasi-reduced dimensional motifs. By…