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Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
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The number of nuclear spins aligned in the lower energy state is slightly greater than those in the higher energy state. In the presence of an external magnetic field, as the spins precess at the Larmor frequency, the excess population results in a net magnetization oriented along the z axis. When a pulse or a short burst of radio waves at the Larmor frequency is applied along the x axis, the coupling of frequencies causes resonance and flips the nuclear spins of the excess population from the...
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
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The spin state of an NMR-active nucleus can have a slight effect on its immediate electronic environment. This effect propagates through the intervening bonds and affects the electronic environments of NMR-active nuclei up to three bonds away; occasionally, even farther. This phenomenon is called spin–spin coupling or J-coupling. Coupling interactions are mutual and result in small changes in the absorption frequencies of both nuclei involved. While nuclei of the same element are involved...
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The roadmap towards AI-assisted pulse programming for solid-state NMR.

Yinglin Li1, Maria Grazia Concilio1, Xueqian Kong2

  • 1Institute of Translational Medicine, Shanghai Jiao Tong University, 200240, Shanghai, China.

Solid State Nuclear Magnetic Resonance
|March 24, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers new solutions for designing solid-state NMR (ssNMR) pulse sequences. AI methods like evolutionary algorithms and deep learning overcome limitations of traditional optimal control approaches for ssNMR.

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Area of Science:

  • Nuclear Magnetic Resonance Spectroscopy
  • Computational Chemistry
  • Materials Science

Background:

  • Solid-state NMR (ssNMR) is crucial for analyzing solids and semi-solids.
  • Traditional pulse sequence design relies on average-Hamiltonian theory and optimal control (OC).
  • OC methods face limitations with strong interactions and hardware constraints.

Purpose of the Study:

  • To review emerging artificial intelligence (AI) approaches for ssNMR pulse sequence design.
  • To highlight the advantages and limitations of current OC methods.
  • To explore AI's potential in addressing ssNMR design bottlenecks.

Main Methods:

  • Review of AI techniques including evolutionary algorithms, deep learning, and reinforcement learning.
  • Analysis of the strengths and weaknesses of gradient-based optimal control (OC).
  • Discussion on how AI can overcome limitations in ssNMR pulse sequence development.

Main Results:

  • AI methods show promise in overcoming limitations of conventional ssNMR pulse sequence design.
  • Evolutionary algorithms, deep learning, and reinforcement learning offer alternative strategies.
  • AI can potentially address challenges posed by strong interactions and hardware constraints.

Conclusions:

  • AI presents a powerful alternative for advancing ssNMR pulse sequence design.
  • AI-driven approaches can enhance the capabilities of ssNMR spectroscopy.
  • Further research into AI for ssNMR is warranted to overcome existing bottlenecks.