Self-doped Mott insulator for parent compounds of nickelate superconductors
PHYSICAL REVIEW B(2020)
- 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

Lifshitz Transition and Frustration of Magnetic Moments in Infinite-Layer NdNiO2 Upon Hole Doping
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Polarity Induced Electronic and Atomic Reconstruction at NdNiO2/SrTiO3 Interfaces
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Orbital and Spin Character of Doped Carriers in Infinite-Layer Nickelates
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Single Particle Tunneling Spectrum of Superconducting Nd1-xSrxNiO2 Thin Films.
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Doping-dependent Character and Possible Magnetic Ordering of NdNiO2
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Charge-stripe Fluctuations in Nd_4Ni_3O_8 As Evidenced by Optical Spectroscopy
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Proposal to Improve Ni-based Superconductors Via Enhanced Charge Transfer
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Magnetic Excitations in Infinite-Layer Nickelates
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Electronic Structure of Superconducting Nickelates Probed by Resonant Photoemission Spectroscopy
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Stability and electronic properties of LaNiO<sub>2</sub>/SrTiO<sub>3</sub>heterostructures
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Microscopic Theory of Superconducting Phase Diagram in Infinite-Layer Nickelates
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Comparative Study of Charge Order in Undoped Infinite-Layer Nickelate Superconductors
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