Artificial Neural Network Processing of Double Electron-Electron Resonance Data
Artificial Neural Network Processing of Double Electron-Electron Resonance Data
We show that artificial neural networks offer a powerful alternative to the established procedures for analysis of Double Electron-Electron Resonance (DEER) data. Simple five layer feed-forward neural networks were trained on a randomly generated synthetic data set. When the output of multiple such networks is combined to make an ensemble guess they are able to extract distance information with accuracy comparable to state-of-the-art methods. The variance in the ensemble output also provides a good estimation of the error in the result.
We then show that neural networks can be trained to predict both the exchange coupling interaction and the distance distribution in parallel. They were shown to provide a confident estimate at the magnitude of the exchange interaction while offering varying success when estimating the distance distribution.
Worswick, Steven Graham
e3f70075-e4f7-4b56-a20a-dbcfcecc90e9
2020
Worswick, Steven Graham
e3f70075-e4f7-4b56-a20a-dbcfcecc90e9
Kuprov, Ilya
bb07f28a-5038-4524-8146-e3fc8344c065
Worswick, Steven Graham
(2020)
Artificial Neural Network Processing of Double Electron-Electron Resonance Data.
Masters Thesis, 110pp.
Record type:
Thesis
(Masters)
Abstract
We show that artificial neural networks offer a powerful alternative to the established procedures for analysis of Double Electron-Electron Resonance (DEER) data. Simple five layer feed-forward neural networks were trained on a randomly generated synthetic data set. When the output of multiple such networks is combined to make an ensemble guess they are able to extract distance information with accuracy comparable to state-of-the-art methods. The variance in the ensemble output also provides a good estimation of the error in the result.
We then show that neural networks can be trained to predict both the exchange coupling interaction and the distance distribution in parallel. They were shown to provide a confident estimate at the magnitude of the exchange interaction while offering varying success when estimating the distance distribution.
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Published date: 2020
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Local EPrints ID: 453032
URI: http://eprints.soton.ac.uk/id/eprint/453032
PURE UUID: b243f5fd-8905-4877-8913-1975f38aca02
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Date deposited: 07 Jan 2022 17:41
Last modified: 17 Mar 2024 03:28
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Author:
Steven Graham Worswick
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