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dc.contributor.authorKeeley, Jake
dc.contributor.authorChoudhury, Tajwar
dc.contributor.authorGalazzo, Laura
dc.contributor.authorBordignon, Enrica
dc.contributor.authorFeintuch, Akiva
dc.contributor.authorGoldfarb, Daniella
dc.contributor.authorRussell, Hannah
dc.contributor.authorTaylor, Michael J.
dc.contributor.authorLovett, Janet E.
dc.contributor.authorEggeling, Andrea
dc.contributor.authorFabregas Ibanez, Luis
dc.contributor.authorKeller, Katharina
dc.contributor.authorYulikov, Maxim
dc.contributor.authorJeschke, Gunnar
dc.contributor.authorKuprov, Ilya
dc.date.accessioned2022-04-06T11:34:37Z
dc.date.available2022-04-06T11:34:37Z
dc.date.issued2022-05
dc.identifier.citationKeeley , J , Choudhury , T , Galazzo , L , Bordignon , E , Feintuch , A , Goldfarb , D , Russell , H , Taylor , M J , Lovett , J E , Eggeling , A , Fabregas Ibanez , L , Keller , K , Yulikov , M , Jeschke , G & Kuprov , I 2022 , ' Neural networks in pulsed dipolar spectroscopy : a practical guide ' , Journal of Magnetic Resonance , vol. 338 , 107186 . https://doi.org/10.1016/j.jmr.2022.107186en
dc.identifier.issn1090-7807
dc.identifier.otherPURE: 278227658
dc.identifier.otherPURE UUID: aca8c9d8-556c-44b1-8cb7-01d8b9e41990
dc.identifier.otherRIS: urn:013B08DAA26FD4A63099C1FDC1353310
dc.identifier.otherORCID: /0000-0002-3561-450X/work/109766684
dc.identifier.otherScopus: 85126954799
dc.identifier.otherWOS: 000821285100002
dc.identifier.urihttps://hdl.handle.net/10023/25147
dc.descriptionThis work was funded by a grant from Leverhulme Trust (RPG-2019-048). Studentship funding and technical support from MathWorks are gratefully acknowledged. This research was supported by grants from NVIDIA and utilised NVIDIA Tesla A100 GPUs through the Academic Grants Programme. We also acknowledge funding from the Royal Society (University Research Fellowship for JEL) and EPSRC (EP/R513337/1 studentship for HR and EP/L015110/1 studentship for MJT).en
dc.description.abstractThis 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.
dc.format.extent14
dc.language.isoeng
dc.relation.ispartofJournal of Magnetic Resonanceen
dc.rightsCopyright 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en
dc.subjectDEERen
dc.subjectPELDORen
dc.subjectRIDMEen
dc.subjectDEERneten
dc.subjectNeural netowrken
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQC Physicsen
dc.subjectQD Chemistryen
dc.subject3rd-DASen
dc.subject.lccQA75en
dc.subject.lccQCen
dc.subject.lccQDen
dc.titleNeural networks in pulsed dipolar spectroscopy : a practical guideen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Physics and Astronomyen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.identifier.doihttps://doi.org/10.1016/j.jmr.2022.107186
dc.description.statusPeer revieweden


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