Symmetry-guided and AI-accelerated design of intercalated transition metal dichalcogenides for antiferromagnetic spintronics
Yu Pang, Yue Gu, Runsheng Zhong, Liyang Zou, Xiaobin Chen, Xiaolong Zou, Wenhui Duan

TL;DR
This paper presents a symmetry-guided, AI-accelerated framework using graph neural networks to discover and design intercalated transition metal dichalcogenides with desirable antiferromagnetic properties for spintronics.
Contribution
It introduces a novel AI-driven approach that efficiently explores vast material configurations to identify promising quantum materials with specific magnetic symmetries.
Findings
Identified 35 altermagnetic and 20 $T\tau$-antiferromagnetic candidates.
Demonstrated tuning of spin-group symmetry to realize d-wave altermagnets.
Revealed $T\tau$-antiferromagnets with high spin-charge conversion efficiency.
Abstract
The advancement of antiferromagnetic spintronics depends on quantum materials with target symmetry-dictated functionalities, however, their systematic discovery is hindered by the immense configurational complexity of the available material space. Here, we introduce a symmetry-guided, AI-accelerated framework incorporating graph neural networks with high generalization ability to overcome this bottleneck. Based on fully intercalated transition metal dichalcogenides (iTMDs) and using only 200 relaxed partially intercalated structures for transfer learning, our model effectively explores more than 100,000 partially intercalated configurations and identifies 35 altermagnetic and 20 -antiferromagnetic ground-state candidates. Interestingly, we show that tuning spin-group symmetry through intercalant arrangement or magnetic ordering realizes a series of d-wave altermagnets in these…
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