Neural networks in pulsed dipolar spectroscopy: A practical guide.

[Display omitted] • A methodological guide to deep neural networks in pulsed dipolar spectroscopy. • Neural networks are unexpectedly good at processing DEER and RIDME data. • Sparsely sampled datasets and uncertainty quantification are supported. • Unlike regularisation methods, DEERNet has no user-adjustable parameters. 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. [ABSTRACT FROM AUTHOR]