Chrome Extension
WeChat Mini Program
Use on ChatGLM

Ferromagnetic Coupling in a Two-Dimensional Cairo Pentagonal Ni2(TCNQ)2 Lattice

Journal of Materiomics(2022)SCI 2区

Univ Jinan | Shandong Univ

Cited 1|Views4
Abstract
Magnetism has revolutionized important technologies, and continues to bring forth new phenomena in emergent materials and reduced dimensions. Here, using first-principles calculations, we demonstrate that the already-synthesized two-dimensional (2D) Ni-tetracyanoquinodimethane (Ni2(TCNQ)2) lattice is a stable ferromagnetism material with multiple spin-polarized Dirac cones. The conical bands in proximity of the Fermi level can be tuned by external tensile strain and show the fourfold degenerate electronic states at the critical tensile strain of ∼2.35 %, whose energy dispersion is consistent with 2D Cairo pentagonal lattice. In addition, spin-orbital coupling can open a band gap at the Dirac point of A, leading to topologically nontrivial electronic states characterized by the non-zero Chern number and the edge states of nanoribbon. Our results offer versatile platforms for the realization of massless spintronics with full-spin polarization in 2D Cairo pentagonal Ni2(TCNQ)2 Lattice.
More
Translated text
Key words
First-principles calculations,Ferromagnetic coupling,Linear dispersion,2D Cairo pentagonal lattice,Quantum anomalous Hall effect
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest