NMR-POISE: On-the-fly, Sample-tailored Optimisation of NMR Experiments

Published: 16 April 2021| Version 1 | DOI: 10.17632/3cztywfzcn.1


The majority of one- and multi-dimensional NMR experiments, indispensable to chemists in many areas of research, are often run with generic or "compromise" parameter values that are not optimised. This is particularly problematic when robust, automated acquisition on a variety of samples is desired. Here we present a Python package, NMR-POISE (Parameter Optimisation by Iterative Spectral Evaluation), with full integration into Bruker’s TopSpin software, that utilises feedback control for on-the-fly, sample-tailored optimisation of NMR experiments. POISE provides a highly extensible and user-friendly framework which allows its core optimisation algorithms to be implemented in a wide variety of scenarios. The data attached herein provide examples of optimisation procedures where POISE can be used to great effect. The raw NMR data is attached here, together with all of the scripts used for processing and plotting this data (which can be used to directly regenerate the figures in the manuscript).


Steps to reproduce

The raw NMR data is in the "datasets" directory, and the processing scripts in the "figures" directory. The scripts can be run as long as this directory structure is maintained, but require v0.3.0 of the "penguins" Python package: this can be installed using the command "pip install penguins=0.3.0" (without quotes). Please refer to the Supporting Information of the POISE paper for more details, including a full description of the individual datasets.


University of Oxford


Chemistry, Analytical Chemistry, Nuclear Magnetic Resonance, Derivative-Free Optimization