Theoretical investigation of the evolution of the topological phase of Bi2_{2}Se3_{3} under mechanical strain

The topological insulating phase results from inversion of the band gap due to spin-orbit coupling at an odd number of time-reversal symmetric points. In Bi$_2$Se$_3$, this inversion occurs at the $\Gamma$ point. For bulk Bi$_2$Se$_3$, we have analyzed the effect of arbitrary strain on the $\Gamma$ point band gap using Density Functional Theory. By computing the band structure both with and without spin-orbit interactions, we consider the effects of strain on the gap via Coulombic interaction and spin-orbit interaction separately. While compressive strain acts to decrease the Coulombic gap, it also increases the strength of the spin-orbit interaction, increasing the inverted gap. Comparison with Bi$_2$Te$_3$ supports the conclusion that effects on both Coulombic and spin-orbit interactions are critical to understanding the behavior of topological insulators under strain, and we propose that the topological insulating phase can be effectively manipulated by inducing strain through chemical substitution

Theoretical Investigation of the Evolution of the Topological Phase of Bi\u3csub\u3e2\u3c/sub\u3eSe\u3csub\u3e3\u3c/sub\u3e under Mechanical Strain

The topological insulating phase results from inversion of the band gap due to spin-orbit coupling at an odd number of time-reversal symmetric points. In Bi2Se3, this inversion occurs at the Γ point. For bulk Bi2Se3, we have analyzed the effect of arbitrary strain on the Γ point band gap using density functional theory. By computing the band structure both with and without spin-orbit interactions, we consider the effects of strain on the gap via Coulombic interaction and spin-orbit interaction separately. While compressive strain acts to decrease the Coulombic gap, it also increases the strength of the spin-orbit interaction, increasing the inverted gap. Comparison with Bi2Te3 supports the conclusion that effects on both Coulombic and spin-orbit interactions are critical to understanding the behavior of topological insulators under strain, and we propose that the topological insulating phase can be effectively manipulated by inducing strain through chemical substitution

Unconventional superconducting pairing in a B20 Kramers Weyl semimetal

Topological superconductors present an ideal platform for exploring nontrivial superconductivity and realizing Majorana boundary modes in materials. However, finding a single-phase topological material with nontrivial superconducting states is a challenge. Here, we predict nontrivial superconductivity in the pristine chiral metal RhGe with a transition temperature of 5.8 K. Chiral symmetries in RhGe enforce multifold Weyl fermions at high-symmetry momentum points and spin-polarized Fermi arc states that span the whole surface Brillouin zone. These bulk and surface chiral states support multiple type-II van Hove singularities that enhance superconductivity in RhGe. Our detailed analysis of superconducting pairing symmetries involving Chiral Fermi pockets in RhGe, indicates the presence of nontrivial superconducting pairing. Our study establishes RhGe as a promising candidate material for hosting mixed-parity pairing and topological superconductivity.Comment: 7 pages, 4 figure

Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics

Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities. To address the limitations, we introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED), exemplified with x-ray photon fluctuation spectroscopy (XPFS) measurements for spin fluctuations. Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. The capability of automatic differentiation from the neural network model is further leveraged for more robust and accurate parameter estimation. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time. Although focusing on XPFS and spin fluctuations, our method can be adapted to other experiments, facilitating more efficient data collection and accelerating scientific discoveries