In situ Learning-Based Spin Engineering of Pulsed Dynamic Nuclear Polarization

TL;DR

This paper introduces an in situ machine learning approach to design efficient pulsed DNP sequences directly on spin systems, improving NMR and MRI sensitivity and quantum sensing capabilities.

Contribution

It presents a novel in situ pulse sequence design method combining Bayesian machine learning and constrained random walks for complex spin systems.

Findings

Efficient broadband DNP sequences were successfully designed in situ.

Experimental protocols matched well with in silico benchmarks.

Method enhances sensitivity in NMR, MRI, and quantum sensing applications.

Abstract

Pulsed Dynamic Nuclear Polarization (DNP) is currently receiving substantial interest as a means to enhance the sensitivity of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) by orders of magnitude. It has also received much attention as a central ingredient in many modalities of electron spin-involved quantum sensing. Relative to spin engineering associated with NMR, the design of efficient pulsed DNP experiments with a broad experimental scope are challenged by large electron-nuclear spin systems, large electron spin-involved interactions, and instrumental non-idealities and limitations. All of this may challenge traditional NMR-like theoretical and numerical pulse sequence engineering. Exploiting state-of-the-art instrumentation and taking advantage of the high sensitivity of DNP relative to NMR, we here demonstrate the use of combinations of Bayesian machine learning methods and constrained random walk procedures to design pulse sequences \textit{in situ}, by experiments, directly on the spin systems responding to spectrometer instructions. For trityl and nitroxide samples, it is demonstrated that efficient broadband DNP pulse sequences can be designed in situ with experimental protocols benchmarked against in silico analogs.