Synthesis and crystal growth

Abstract:

We present experimental and theoretical evidence of novel bound state formation in the low transverse field ordered phase of the quasi-one-dimensional Ising-like material CoNb2O6. High-resolution single-crystal inelastic neutron scattering measurements observe that small transverse fields lead to a breakup of the spectrum into three parts, each evolving very differently upon increasing field. This can be naturally understood starting from the excitations of the ordered phase of the transverse field Ising model, domain wall quasiparticles (solitons). Here, the transverse field and a staggered off-diagonal exchange create one-soliton hopping terms with opposite signs. We show that this leads to a rich spectrum and a special field, when the strengths of the off-diagonal exchange and transverse field match, at which solitons become localized; the highest field investigated is very close to this special regime. We solve this case analytically and find three two-soliton continua, along with three novel bound states. Perturbing away from this novel localized limit, we find very good qualitative agreement with the experimental data. We also present calculations using exact diagonalization of a recently refined Hamiltonian model for CoNb2O6 and using diagonalization of the two-soliton subspace, both of which provide a quantitative agreement with the observed spectrum. The theoretical models qualitatively and quantitatively capture a variety of nontrivial features in the observed spectrum, providing insight into the underlying physics of bound state formation.

Abstract:

The Ising chain realizes the fundamental paradigm of spin fractionalization, where locally flipping a spin creates two domain walls (spinons) that can separate apart at no energy cost. In a quasi-one-dimensional system, the mean-field effects of the weak three-dimensional couplings confine the spinons into a Zeeman ladder of two-spinon bound states. Here, we experimentally tune the confinement potential between spinons in the quasi-one-dimensional Ising ferromagnet Co⁢Nb₂⁢O₆ by means of an applied magnetic field with a large component along the Ising direction. Using high-resolution single crystal inelastic neutron scattering, we directly observe how the spectrum evolves from the limit of very weak confinement at low field (with many closely spaced bound states with energies scaling as the field strength to the power 2/3) to very strong confinement at high field (where it consists of a magnon and a dispersive two-magnon bound state, with a linear field dependence). At intermediate fields, we explore how the higher-order bound states disappear from the spectrum as they move to higher energies and overlap with the two-particle continuum. By performing a global fit to the observed spectrum in zero field and high field applied along two orthogonal directions, combined with a quantitative parametrization of the interchain couplings, we propose a refined single-chain and interchain Hamiltonian that quantitatively reproduces the dispersions of all observed modes and their field dependence.

Abstract:

The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system. Analysis of these excitations is usually carried out by measuring the dynamical structure factor, S(Q, $\omega$), with inelastic neutron or x-ray scattering techniques and comparing this against a calculated dynamical model. Here, we develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. We benchmark this approach on a Linear Spin Wave Theory (LSWT) simulator and advanced inelastic neutron scattering data from the square-lattice spin-1 antiferromagnet La$_2$NiO$_4$. We find that the model predicts the unknown parameters with excellent agreement relative to analytical fitting. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data, without the need for human-guided peak finding and fitting algorithms. This prototypical approach promises a new technology for this field to automatically detect and refine more advanced models for ordered quantum systems.Comment: 12 pages, 7 figure