SearcharxivSearch

arXiv subjects

Charles Snider

Publications and source records attributed to Charles Snider.

3 recordsLinked to original sources

Dynamic Simulations of Strongly Coupled Spin Ensembles for Inferring Nature of Electronic Correlations from Nuclear Magnetic Resonance

We develop an efficient package for the simulation of nuclear magnetic resonance spin echo experiments to study the effects of strong electronic spin correlations on the dynamics of the nuclear spin ensemble. A mean-field model is used to study correlated electronic phases through their hyperfine interaction with nuclear spins. We explore the dynamics of the interacting nuclear ensemble and discuss the key behaviors of the system. In particular, we classify the types of temporal asymmetry that the interaction induces in the system as well as a pulse-dependent shift in the spectral domain. Using these results, we discuss how careful measurement of the pulse-dependent shift can be used to extract information about the anisotropy of the electronic interaction and how these results represent a novel tool for the examination of exotic NMR signatures in strongly correlated materials. Finally, we review specific aspects of the simulation package developed for our exploration and give explicit examples where package can be used to infer range and anisotropy of electronic correlations. In particular, we discuss its structure, accuracy, and the technical merits of the various approximations used to model the nuclear spin ensemble.

cond-mat.str-el

Machine learning assisted determination of electronic correlations from magnetic resonance

In the presence of strong electronic spin correlations, the hyperfine interaction imparts long-range coupling between nuclear spins. Efficient protocols for the extraction of such complex information about electron correlations via magnetic response are not well known. Here, we study how machine learning can extract material parameters and help interpret magnetic response experiments. A low-dimensional representation that classifies the total interaction strength is discovered by unsupervised learning. Supervised learning generates models that predict the spatial extent of electronic correlations and the total interaction strength. Our work demonstrates the utility of artificial intelligence in the development of new probes of quantum systems, with applications to experimental studies of strongly correlated materials.

cond-mat.dis-nn

Signatures of electronic correlations and spin-susceptibility anisotropy in nuclear magnetic resonance

We present a methodology for probing the details of electronic susceptibility through minimally-invasive nuclear magnetic resonance techniques. Specifically, we classify electron-mediated long-range interactions in an ensemble of nuclear spins by revealing their effect on simple spin echo experiments. We find that pulse strength and applied field orientation dependence of these spin echo measurements resolves the spatial extent and anisotropy of electronic spin susceptibility. This work provides an alternate explanation to NMR results in superconducting and magnetically-ordered systems. The methodology has direct applications for sensing and characterizing emergent electronic phases.

cond-mat.str-el