# 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.

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Examples

Project details

## How to cite¶

If you use this work in your publication, please cite the following.

• 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). https://doi.org/10.1063/5.0023345.

• Deepansh J. Srivastava, Maxwell Venetos, Philip J. Grandinetti, Shyam Dwaraknath, & Alexis McCarthy. (2021, May 26). mrsimulator: v0.6.0 (Version v0.6.0). Zenodo. http://doi.org/10.5281/zenodo.4814638

Additionally, if you use the CSDM data model, please consider citing

• Srivastava DJ, Vosegaard T, Massiot D, Grandinetti PJ (2020) Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data. PLOS ONE 15(1): e0225953. https://doi.org/10.1371/journal.pone.0225953