Workflows#

This section describes the main command-line workflows provided by simpnmr. Each workflow is configured using a strict YAML input file: unknown keys are not allowed and will result in a configuration error.

For a complete reference of available configuration blocks and their semantics, see Input YAML Files.

Conventions#

  • Commands are shown as they should be typed in a terminal.

  • <input.yml> denotes a user-supplied YAML configuration file.

  • Paths in YAML may be absolute or relative to the working directory.

pNMR Prediction#

Runs a pNMR prediction workflow using supplied tensors and molecular structure information.

Run#

simpnmr predict <input.yml>

Minimal Input Example#

project:
  name: run_name

nuclei:
  include: H

hyperfine:
  method: dft
  file: hfc/file.out

susceptibility:
  file: chi/file.out
  format: orca_nev
  temperatures: [100, 200, 300]

Note

In prediction workflows, list-like inputs (e.g. susceptibility tensors) are processed positionally.

Optional Additions#

You may additionally supply optional blocks to refine the prediction workflow. Refer to Input YAML Files for the full contract and conditional requirements.

  • chem_labels: provide chemical grouping labels (enables averaging).

  • diamagnetic / diamagnetic_ref: include diamagnetic corrections.

  • relaxation: apply relaxation-based line broadening or weighting.

Magnetic susceptibility fitting#

Fits a magnetic susceptibility tensor model to experimental data.

Run#

simpnmr fit_susc <input.yml>

Minimal input example#

project:
  name: fit_run

hyperfine:
  method: dft
  file: hfc/file.out

chem_labels:
  file: chem_labels.csv

nuclei:
  include: H

experiment:
  files: exp/peaks_*.csv

assignment:
  method: fixed

susc_fit:
  type: isoaxrho
  variables:
    iso: [fit, 0.0]
    ax: [fit, 0.0]
    rho_over_ax: [fix, 0.0]

Optional additions#

  • susc_vt: temperature-dependent susceptibility fitting (optional and model-dependent).

  • diamagnetic / diamagnetic_ref: include diamagnetic corrections.

  • assignment with permute: explore assignments within user-defined groups.

Notes#

  • Some optional blocks are model- or method-dependent (e.g. quantum numbers for certain hyperfine sources, or TIP handling in susc_vt).

  • Ensure that configuration inputs are ordered consistently where positional processing is used.