Webinar: From Project to Platform: New Resources on PSDI – Session 4

Challenge

• • NMR spectra of paramagnetic metal complexes in solution are notoriously difficult to interpret, as unpaired electrons produce large chemical shifts, broadened lineshapes, and temperature-dependent behaviour that standard diamagnetic analysis tools cannot handle.
  • Extracting meaningful electronic structure information (magnetic susceptibility tensors, correlation times, spin-Hamiltonian parameters) from pNMR data requires combining experimental spectra with ab initio calculations, a workflow that today remains fragmented, manual, and inaccessible to many researchers.

Approach

• • SimpNMR is a Python package that streamlines pNMR analysis by directly incorporating outputs of ab initio calculations.
  • It provides an end-to-end workflow for paramagnetic complexes in solution, including:
    • prediction of 1D NMR spectra (e.g. 1H, 13C)
    • assignment of experimental peaks to molecular sites
    • fitting of the magnetic susceptibility tensor and correlation times to experimental pNMR data
  • With variable-temperature experiments, SimpNMR extracts spin-Hamiltonian parameters such as the g-tensor, and in certain cases the D-tensor, directly from solution pNMR data, information typically accessible only from EPR or SQUID magnetometry.

Innovation

• • SimpNMR transforms pNMR analysis from a bespoke, expert-only procedure into a reproducible, scriptable Python workflow that bridges computational and experimental chemistry.
  • SimpNMR_DB, a curated companion database, stores the input data required for SimpNMR analysis, enabling:
    • reuse and benchmarking of ab initio inputs across complexes
    • reproducible, shareable analysis pipelines
    • accelerated discovery by lowering the barrier to quantitative pNMR interpretation
  • Together, SimpNMR and SimpNMR_DB open up solution pNMR as a practical route to electronic-structure parameters of paramagnetic metal complexes.