Capturing dynamical correlations using implicit neural representations.
2023; 14 (1): 5852
Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S(Q, ω), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages 'neural implicit representations' that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La2NiO4, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.
View details for DOI 10.1038/s41467-023-41378-4
View details for PubMedID 37730824
View details for PubMedCentralID 8662964
From Stoner to local moment magnetism in atomically thin Cr2Te3.
2023; 14 (1): 5340
The field of two-dimensional (2D) ferromagnetism has been proliferating over the past few years, with ongoing interests in basic science and potential applications in spintronic technology. However, a high-resolution spectroscopic study of the 2D ferromagnet is still lacking due to the small size and air sensitivity of the exfoliated nanoflakes. Here, we report a thickness-dependent ferromagnetism in epitaxially grown Cr2Te3 thin films and investigate the evolution of the underlying electronic structure by synergistic angle-resolved photoemission spectroscopy, scanning tunneling microscopy, x-ray absorption spectroscopy, and first-principle calculations. A conspicuous ferromagnetic transition from Stoner to Heisenberg-type is directly observed in the atomically thin limit, indicating that dimensionality is a powerful tuning knob to manipulate the novel properties of 2D magnetism. Monolayer Cr2Te3 retains robust ferromagnetism, but with a suppressed Curie temperature, due to the drastic drop in the density of states near the Fermi level. Our results establish atomically thin Cr2Te3 as an excellent platform to explore the dual nature of localized and itinerant ferromagnetism in 2D magnets.
View details for DOI 10.1038/s41467-023-40997-1
View details for PubMedID 37660171
View details for PubMedCentralID PMC10475109
Traces of electron-phonon coupling in one-dimensional cuprates.
2023; 14 (1): 3129
The appearance of certain spectral features in one-dimensional (1D) cuprate materials has been attributed to a strong, extended attractive coupling between electrons. Here, using time-dependent density matrix renormalization group methods on a Hubbard-extended Holstein model, we show that extended electron-phonon (e-ph) coupling presents an obvious choice to produce such an attractive interaction that reproduces the observed spectral features and doping dependence seen in angle-resolved photoemission experiments: diminished 3kF spectral weight, prominent spectral intensity of a holon-folding branch, and the correct holon band width. While extended e-ph coupling does not qualitatively alter the ground state of the 1D system compared to the Hubbard model, it quantitatively enhances the long-range superconducting correlations and suppresses spin correlations. Such an extended e-ph interaction may be an important missing ingredient in describing the physics of the structurally similar two-dimensional high-temperature superconducting layered cuprates, which may tip the balance between intertwined orders in favor of uniform d-wave superconductivity.
View details for DOI 10.1038/s41467-023-38408-6
View details for PubMedID 37253739
View details for PubMedCentralID PMC10229634
- Enhanced superconductivity by near-neighbor attraction in the doped extended Hubbard model PHYSICAL REVIEW B 2023; 107 (20)
- A Quantum-Inspired Tensor Network Algorithm for Constrained Combinatorial Optimization Problems FRONTIERS IN PHYSICS 2022; 10
- Testing the data framework for an AI algorithm in preparation for high data rate X-ray facilities IEEE. 2022: 1-9
- Gapless spin liquid and pair density wave of the Hubbard model on three-leg triangular cylinders NEW JOURNAL OF PHYSICS 2021; 23 (12)
- Precursor of pair-density wave in doping Kitaev spin liquid on the honeycomb lattice NPJ QUANTUM MATERIALS 2021; 6 (1)
- Doping Quantum Spin Liquids on the Kagome Lattice ADVANCED QUANTUM TECHNOLOGIES 2021