Computational Discovery of Two-Dimensional Rare-Earth Iodides: Promising Ferrovalley Materials for Valleytronics
Abhishek Sharan, Stephan Lany, and Nirpendra Singh

TL;DR
This paper reports the computational discovery of 17 new two-dimensional rare-earth iodide materials with intrinsic valley polarization and ferromagnetism, promising for valleytronics applications such as nonvolatile memory and valley filters.
Contribution
The study introduces a novel computational approach combining Kinetically Limited Minimization and first-principles calculations to predict new Ferrovalley materials with tunable valley polarization.
Findings
Discovered 17 new rare-earth iodide Ferrovalley materials.
Monolayers exhibit large intrinsic valley polarization (15-143 meV).
Predicted valley Hall effect and non-zero Berry curvature in 2H phase monolayers.
Abstract
Two-dimensional Ferrovalley materials with intrinsic valley polarization are rare but highly promising for valley-based nonvolatile random access memory and valley filter. Using Kinetically Limited Minimization (KLM), an unconstrained crystal structure prediction algorithm, and prototype sampling based on first-principles calculations, we have discovered 17 new Ferrovalley materials (rare-earth iodides RI, where R is a rare-earth element belonging to Sc, Y, or La-Lu, and I is Iodine). The rare-earth iodides are layered and demonstrate 2H, 1T, or 1T phase as the ground-state in bulk, analogous to transition metal dichalcogenides (TMDCs). The calculated exfoliation energy of monolayers is comparable to that of graphene and TMDCs, suggesting possible experimental synthesis. The monolayers in the 2H phase exhibit two-dimensional ferromagnetism due to unpaired electrons in and…
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Taxonomy
Topics2D Materials and Applications · Machine Learning in Materials Science
