Magnetic Structures Database from Symmetry-aided High-Throughput Calculations
Hanjing Zhou, Yuxuan Mu, Dingwen Zhang, Hangbing Chu, Erjun Kan, Chun-Gang Duan, Di Wang, Huimei Liu, Xin-Gao Gong, and Xiangang Wan

TL;DR
This paper introduces a symmetry-based method to efficiently generate candidate magnetic structures, enabling the creation of a large magnetic materials database and facilitating the discovery of topological magnetic phases.
Contribution
The authors develop a symmetry-aided approach that reduces the search space for magnetic structures, significantly improving the efficiency of first-principles calculations and database creation.
Findings
Successfully identified magnetic structures for 83.8% of benchmark materials
Built a magnetic structure database with 2,906 materials
Discovered 1,070 topological magnetic phase candidates
Abstract
Magnetic structures, which play a central role in determining their physical properties, are known for only very limited compounds. Traditional theoretical approaches to predicting magnetic structures predominantly rely on first-principles calculations. A key challenge of these methods is their requirement for initial magnetic configurations as inputs, which theoretically possess infinite possibilities. In this work, we introduce a strategy based on irreducible representation basis vectors that effectively narrows down the vast space of potential magnetic configurations to a finite set, typically comprising around 20 candidates per material. Despite this significant reduction, the compact input sets generated by our method already encompass the experimental magnetic structures for 253 out of 302 benchmark materials (83.8%) from the MAGNDATA database. These materials have propagation…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Condensed Matter Physics · Inorganic Chemistry and Materials
