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

Capturing dynamical correlations using implicit neural representations

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

3D Heisenberg universality in the Van der Waals antiferromagnet NiPS3_3

Van der Waals (vdW) magnetic materials are comprised of layers of atomically thin sheets, making them ideal platforms for studying magnetism at the two-dimensional (2D) limit. These materials are at the center of a host of novel types of experiments, however, there are notably few pathways to directly probe their magnetic structure. We report the magnetic order within a single crystal of NiPS$_3$ and show it can be accessed with resonant elastic X-ray diffraction along the edge of the vdW planes in a carefully grown crystal by detecting structurally forbidden resonant magnetic X-ray scattering. We find the magnetic order parameter has a critical exponent of $\beta\sim0.36$, indicating that the magnetism of these vdW crystals is more adequately characterized by the three-dimensional (3D) Heisenberg universality class. We verify these findings with first-principle density functional theory, Monte-Carlo simulations, and density matrix renormalization group calculations

The low-field susceptibility of the superconducting state of Sr2RuO4

Sr2RuO4 has long been considered as a textbook example of a system where superconductivity develops from a strongly correlated Fermi liquid. It has generated considerable interest as a test of theory for many reasons including: (i) it has a relatively simple layered structure; (ii) very clean samples can be prepared and, (iii) its magnetic excitations are well-characterised. New NMR Knight shift and polarized neutron scattering (PNS) measurements of the susceptibility in the superconducting state have brought into question the accepted pairing wavefunction. Thus, in the last year numerous candidate superconducting states based on magnetically mediated pairing and ab-initio calculations have been proposed. Here we propose to make further PNS measurements to constrain the allowed superconducting states

Bayesian 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 materials physics, which often suffer from the scarcity of large-scale facility resources, such as x-ray or neutron scattering centers. To address these limitations, we introduce a methodology that leverages the Bayesian optimal experimental design paradigm to efficiently uncover key quantum spin fluctuation parameters from x-ray photon fluctuation spectroscopy (XPFS) data. Our method is compatible with existing theoretical simulation pipelines and can also be used in combination with fast machine learning surrogate models in the event that real-time simulations are unfeasible. Our numerical benchmarks demonstrate the superior performance in predicting model parameters and in delivering more informative measurements within limited experimental time. Our method can be adapted to many different types of experiments beyond XPFS and spin fluctuation studies, facilitating more efficient data collection and accelerating scientific discoveries

Tophus resolution with pegloticase: a prospective dual-energy CT study

Objective: To investigate the effect of intensive lowering of serum uric acid (SUA) levels by pegloticase on the resolution of tophi in patients with refractory gout. Methods: Descriptive study in patients with refractory gout receiving pegloticase treatment. SUA levels were measured before and after each infusion. Dual-energy CT (DECT) scans were taken from all patients before the first infusion and after the last infusion. Computerised tophus volumes were calculated for the baseline and follow-up assessments and compared with each other. Results: 10 patients with refractory gout and baseline mean SUA level of 8.1 mg/dL were enrolled. Patients were treated for a mean of 13.3 weeks. Pegloticase effectively reduced tophi in all patients showing a decrease in volume by 71.4%. Responders, showing reduction of SUA level below 6 mg/dL during at least 80% of the treatment time, were virtually cleared from tophi (−94.8%). Dependent on their anatomical localisation, resolution of tophi showed different dynamics, with articular tophi showing fast, and tendon tophi slow, resolution. Conclusions: Tophi are highly sensitive to pegloticase treatment, particularly when located at articular sites. Debulking of disease and a tophus-free state can be reached within a few months of pegloticase treatment. DECT allows for comprehensively assessing tophus burden and monitoring treatment responses

Capturing dynamical correlations using implicit neural representations

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