Machine learning for laser-induced electron diffraction imaging of molecular structures

Machine learning for laser-induced electron diffraction imaging of molecular structures

Diffraction imaging offers high spatiotemporal resolution, but fitting complex molecular structure data is expensive. Here, the authors use a machine learning algorithm with a convolutional neural network to retrieve a complex and large molecule, fenchone (C10H16O), from laser-induced electron diffr...

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Autores principales: Xinyao Liu, Kasra Amini, Aurelien Sanchez, Blanca Belsa, Tobias Steinle, Jens Biegert
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Chemistry
QD1-999
Acceso en línea:https://doaj.org/article/d2b26194b0f3431e99966ab82846d27b
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spelling oai:doaj.org-article:d2b26194b0f3431e99966ab82846d27b2021-11-14T12:07:19ZMachine learning for laser-induced electron diffraction imaging of molecular structures10.1038/s42004-021-00594-z2399-3669https://doaj.org/article/d2b26194b0f3431e99966ab82846d27b2021-11-01T00:00:00Zhttps://doi.org/10.1038/s42004-021-00594-zhttps://doaj.org/toc/2399-3669Diffraction imaging offers high spatiotemporal resolution, but fitting complex molecular structure data is expensive. Here, the authors use a machine learning algorithm with a convolutional neural network to retrieve a complex and large molecule, fenchone (C10H16O), from laser-induced electron diffraction data without the need for fitting or ab initio calculations.Xinyao LiuKasra AminiAurelien SanchezBlanca BelsaTobias SteinleJens BiegertNature PortfolioarticleChemistryQD1-999ENCommunications Chemistry, Vol 4, Iss 1, Pp 1-7 (2021)
institution DOAJ
collection DOAJ
language EN
topic Chemistry
QD1-999
spellingShingle Chemistry
QD1-999
Xinyao Liu
Kasra Amini
Aurelien Sanchez
Blanca Belsa
Tobias Steinle
Jens Biegert
Machine learning for laser-induced electron diffraction imaging of molecular structures
description Diffraction imaging offers high spatiotemporal resolution, but fitting complex molecular structure data is expensive. Here, the authors use a machine learning algorithm with a convolutional neural network to retrieve a complex and large molecule, fenchone (C10H16O), from laser-induced electron diffraction data without the need for fitting or ab initio calculations.
format article
author Xinyao Liu
Kasra Amini
Aurelien Sanchez
Blanca Belsa
Tobias Steinle
Jens Biegert
author_facet Xinyao Liu
Kasra Amini
Aurelien Sanchez
Blanca Belsa
Tobias Steinle
Jens Biegert
author_sort Xinyao Liu
title Machine learning for laser-induced electron diffraction imaging of molecular structures
title_short Machine learning for laser-induced electron diffraction imaging of molecular structures
title_full Machine learning for laser-induced electron diffraction imaging of molecular structures
title_fullStr Machine learning for laser-induced electron diffraction imaging of molecular structures
title_full_unstemmed Machine learning for laser-induced electron diffraction imaging of molecular structures
title_sort machine learning for laser-induced electron diffraction imaging of molecular structures
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/d2b26194b0f3431e99966ab82846d27b
work_keys_str_mv AT xinyaoliu machinelearningforlaserinducedelectrondiffractionimagingofmolecularstructures
AT kasraamini machinelearningforlaserinducedelectrondiffractionimagingofmolecularstructures
AT aureliensanchez machinelearningforlaserinducedelectrondiffractionimagingofmolecularstructures
AT blancabelsa machinelearningforlaserinducedelectrondiffractionimagingofmolecularstructures
AT tobiassteinle machinelearningforlaserinducedelectrondiffractionimagingofmolecularstructures
AT jensbiegert machinelearningforlaserinducedelectrondiffractionimagingofmolecularstructures
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