  This is a methodological guide to the use of deep neural networks in the
processing of pulsed dipolar spectroscopy (PDS) data encountered in structural
biology, organic photovoltaics, photosynthesis research, and other domains
featuring long-lived radical pairs and paramagnetic metal ions. PDS uses
distance dependence of magnetic dipolar interactions; measuring a single
well-defined distance is straightforward, but extracting distance distributions
is a hard and mathematically ill-posed problem requiring careful regularisation
and background fitting. Neural networks do this exceptionally well, but their
"robust black box" reputation hides the complexity of their design and training
- particularly when the training dataset is effectively infinite. The objective
of this paper is to give insight into training against simulated databases, to
discuss network architecture choices, to describe options for handling DEER
(double electron-electron resonance) and RIDME (relaxation-induced dipolar
modulation enhancement) experiments, and to provide a practical data processing
flowchart.
