Neural networks in pulsed dipolar spectroscopy: a practical guide
Abstract
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.
Cite
@article{arxiv.2106.07465,
title = {Neural networks in pulsed dipolar spectroscopy: a practical guide},
author = {Jake Keeley and Tajwar Choudhury and Laura Galazzo and Enrica Bordignon and Akiva Feintuch and Daniella Goldfarb and Hannah Russell and Michael J. Taylor and Janet E. Lovett and Andrea Eggeling and Luis Fabregas Ibanez and Katharina Keller and Maxim Yulikov and Gunnar Jeschke and Ilya Kuprov},
journal= {arXiv preprint arXiv:2106.07465},
year = {2022}
}