Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019)

With artificial intelligence (AI), we learn the relationship between molecular structure and properties. In article number 1801367, Patrick Rinke and co‐workers build a deep learning AI spectroscopist that can make predictions for molecular spectra instantly and at no further cost for the end user....

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Detalles Bibliográficos
Autores principales: Ghosh, Kunal, Stuke, Annika, Todorović, Milica, Jørgensen, Peter Bjørn, Schmidt, Mikkel N., Vehtari, Aki, Rinke, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Inside Front Cover
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6498127/
http://dx.doi.org/10.1002/advs.201970053
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author Ghosh, Kunal
Stuke, Annika
Todorović, Milica
Jørgensen, Peter Bjørn
Schmidt, Mikkel N.
Vehtari, Aki
Rinke, Patrick
author_facet Ghosh, Kunal
Stuke, Annika
Todorović, Milica
Jørgensen, Peter Bjørn
Schmidt, Mikkel N.
Vehtari, Aki
Rinke, Patrick
author_sort Ghosh, Kunal
collection PubMed
description With artificial intelligence (AI), we learn the relationship between molecular structure and properties. In article number 1801367, Patrick Rinke and co‐workers build a deep learning AI spectroscopist that can make predictions for molecular spectra instantly and at no further cost for the end user. AI spectroscopy will greatly accelerate the way in which science is done and aid materials discovery and design. [Image: see text]
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spelling pubmed-64981272019-05-07 Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019) Ghosh, Kunal Stuke, Annika Todorović, Milica Jørgensen, Peter Bjørn Schmidt, Mikkel N. Vehtari, Aki Rinke, Patrick Adv Sci (Weinh) Inside Front Cover With artificial intelligence (AI), we learn the relationship between molecular structure and properties. In article number 1801367, Patrick Rinke and co‐workers build a deep learning AI spectroscopist that can make predictions for molecular spectra instantly and at no further cost for the end user. AI spectroscopy will greatly accelerate the way in which science is done and aid materials discovery and design. [Image: see text] John Wiley and Sons Inc. 2019-05-03 /pmc/articles/PMC6498127/ http://dx.doi.org/10.1002/advs.201970053 Text en © 2019 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Inside Front Cover
Ghosh, Kunal
Stuke, Annika
Todorović, Milica
Jørgensen, Peter Bjørn
Schmidt, Mikkel N.
Vehtari, Aki
Rinke, Patrick
Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019)
title Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019)
title_full Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019)
title_fullStr Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019)
title_full_unstemmed Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019)
title_short Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019)
title_sort machine learning: deep learning spectroscopy: neural networks for molecular excitation spectra (adv. sci. 9/2019)
topic Inside Front Cover
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6498127/
http://dx.doi.org/10.1002/advs.201970053
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