Abstract:Topological insulators (TIs) are considered as ideal platforms for generating large spin Hall conductivity (SHC), however, the bulk carrier problem, which is unavoidable in TIs, hinders their practical applications. Recently, topological semimetals (TSMs) have been proposed to achieve large SHC to replace TIs. However, the ideal TSM candidates with large SHC are still lacking. In terms of first-principles calculations, we predict that Ta3As family compounds exhibit complex crossing nodal-lines (CNL) properties… Show more
“…In addition to the already synthesized materials, those unrealized hypothetical materials provide potential opportunities for energy and environmental materials, − structural materials, and electronic devices, , and as shown in Table and Figure b, many of these materials that are estimated stable by E hull ML might have underestimated stability in the MP database.…”
The application of machine learning to predict materials properties measured by experiments are valuable yet difficult due to the limited amount of experimental data. In this work, we use a multifidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the Perdew−Burke−Ernzerhof (PBE) functional with linear correction, PBEsol, and metageneralized gradient approximation (meta-GGA) functionals (SCAN and r 2 SCAN), and it outperforms the hotly studied deep neural network-based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database and discover materials with underestimated stability. The multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output.
“…In addition to the already synthesized materials, those unrealized hypothetical materials provide potential opportunities for energy and environmental materials, − structural materials, and electronic devices, , and as shown in Table and Figure b, many of these materials that are estimated stable by E hull ML might have underestimated stability in the MP database.…”
The application of machine learning to predict materials properties measured by experiments are valuable yet difficult due to the limited amount of experimental data. In this work, we use a multifidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the Perdew−Burke−Ernzerhof (PBE) functional with linear correction, PBEsol, and metageneralized gradient approximation (meta-GGA) functionals (SCAN and r 2 SCAN), and it outperforms the hotly studied deep neural network-based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database and discover materials with underestimated stability. The multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output.
“…Such a discrepancy between the SOT conductivity and the critical switching current density have also been reported by , and the physics at work might depend on the saturation magnetization, the Dzyaloshinskii-Moriya interaction, and the magnetic damping, which require future efforts to understand. Recently, another topological semimetal, Ta 3 As, was predicted to has a large SOT conductivity around 150 × 10 3 (ħ/2e) (Ωm) -1 (Hou et al, 2021). This prediction promotes further research on SOT devices based on 2D semimetals with topological and superconductivity properties.…”
Spin-orbit torque (SOT) provides an efficient approach to control the magnetic state and dynamics in different classes of materials. Recent years, the crossover between two-dimensional van der Waals (2D vdW) materials and SOT opens a new prospect to push SOT devices to the 2D limit. In this mini-review, we summarize the latest progress in 2D vdW materials for SOT applications, highlighting the comparison of the performance between devices with various structures. It is prospected that the large family of 2D vdW materials and numerous combinations of heterostructures will widely extend the material choices and bring new opportunities to SOT devices in the future.
“…Recently, the topic of band crossings has been extensively revisited in the context of topological characterization of the electronic structure of semimetals. [5][6][7][8][9][10] It is well established that if nonsymmorphic symmetries are present in a crystalline lattice, they will enforce the crossing of electronic bands. [11][12][13][14] Dirac and/or Weyl points are formed and they are globally stable.…”
Section: Introductionmentioning
confidence: 99%
“…[11][12][13][14] Dirac and/or Weyl points are formed and they are globally stable. [5][6][7][8][9][10] The band crossing can also occur at generic k points in the Brillouin zone and is independent of the symmetry properties of the system, including the lattice symmetries and the reality of the Hamiltonian. This case is called accidental band crossings that were first discussed by Herring in 1937.…”
Dirac points are found to emerge due to the crossing of bands in the electronic structure of bilayer graphene for configurations in which the alignment between two hexagonal lattices preserves the parallelism of the armchair/zigzag lines between two layers. On the base of electronic calculations using a tight-binding model for the π bands it is shown that the crossing of the energy-band dispersion curves occurs in the vicinity of the corner points of the hexagonal Brillouin zone. Group representation theory analysis confirms the emergence of such Dirac points. It is demonstrated that the band crossings at generic k points are guaranteed by the compatibility relations between the symmetries of eigenstates at the high-symmetry k points in the Brillouin zone. The presence of Dirac points governs the geometrical properties of the energy surfaces, and thus the topological structure of the Fermi energy surface and the energy spectrum.
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