Welcome to Mrinversion documentation!

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About

The mrinversion python package is based on the statistical learning technique for determining the distribution of the magnetic resonance (NMR) tensor parameters from two-dimensional NMR spectra correlating the isotropic to anisotropic frequencies. The library utilizes the mrsimulator package for generating solid-state NMR spectra and scikit-learn package for statistical learning.


Features

The mrinversion package includes the inversion of a two-dimensional solid-state NMR spectrum of dilute spin-systems to a three-dimensional distribution of tensor parameters. At present, we support the inversion of

  • Magic angle turning (MAT), Phase adjusted spinning sidebands (PASS), and similar spectra correlating the isotropic chemical shift resonances to pure anisotropic spinning sideband resonances into a three-dimensional distribution of nuclear shielding tensor parameters, \(\rho(\delta_\text{iso}, \zeta_\sigma, \eta_\sigma)\), where \(\delta_\text{iso}\) is the isotropic chemical shift, and \(\zeta_\sigma\) and \(\eta_\sigma\), are the shielding anisotropy and asymmetry parameters, respectively, defined using the Haeberlen convention.

  • Magic angle flipping (MAF) spectra correlating the isotropic chemical shift resonances to pure anisotropic resonances into a three-dimensional distribution of nuclear shielding tensor parameters, \(\rho(\delta_\text{iso}, \zeta_\sigma, \eta_\sigma)\), where \(\delta_\text{iso}\) is the isotropic chemical shift, and \(\zeta_\sigma\) and \(\eta_\sigma\), are the shielding anisotropy and asymmetry parameters, respectively, defined using the Haeberlen convention.


View our example gallery

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Examples

Examples

Project details

Project details

Publication

  • Srivastava, D. J.; Grandinetti P. J., Statistical learning of NMR tensors from 2D isotropic/anisotropic correlation nuclear magnetic resonance spectra, J. Chem. Phys. 153, 134201 (2020). DOI:10.1063/5.0023345.


Indices and tables