Neural networks in pulsed dipolar spectroscopy: A practical guide

Article Article scientifique Article Neural networks in pulsed dipolar spectroscopy: A practical guide eng 2022-05 author Keeley Jake author Choudhury Tajwar author Galazzo Laura 974693 author Bordignon Enrica 0000-0003-2450-5161 973830 Département de chimie physique (CHIFI) author Feintuch Akiva author Goldfarb Daniella author Russell Hannah author Taylor Michael J. author Lovett Janet E. author Eggeling Andrea author Fábregas Ibáñez Luis author Keller Nicola Katharina 92528 author Yulikov Maxim author Jeschke Gunnar author Kuprov Ilya 1-14 107186 10.1016/j.jmr.2022.107186 35344921 1090-7807 https://linkinghub.elsevier.com/retrieve/pii/S1090780722000441 <p>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.</p> DEER DEERNet Neural network PELDOR RIDME Dewey 540 38 Département de chimie physique 142 false Journal of magnetic resonance 338 195b48e9-033b-46a8-8c89-93efffea6e08 Article (Published version) Public BORDIGNON38.pdf 2984068 application/pdf CC BY-4.0 010fabd5b5ba331c115bad3359f47e362c046c0ed208564ec9f2cea13388d290 openAccess Faculté des sciences / Section de chimie et biochimie / Département de chimie physique

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Neural networks in pulsed dipolar spectroscopy: A practical guide	Journal of magnetic resonance			2022	101	165

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Keller, Nicola Katharina