# Welcome to Mrinversion documentation!¶

 Deployment Build Status License Metrics GitHub Citations

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

Examples

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.