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Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accura...

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Detalles Bibliográficos
Autores principales: Lansford, Joshua L., Vlachos, Dionisios G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089992/
https://www.ncbi.nlm.nih.gov/pubmed/32251293
http://dx.doi.org/10.1038/s41467-020-15340-7
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author Lansford, Joshua L.
Vlachos, Dionisios G.
author_facet Lansford, Joshua L.
Vlachos, Dionisios G.
author_sort Lansford, Joshua L.
collection PubMed
description There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap.
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spelling pubmed-70899922020-03-26 Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials Lansford, Joshua L. Vlachos, Dionisios G. Nat Commun Article There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap. Nature Publishing Group UK 2020-03-23 /pmc/articles/PMC7089992/ /pubmed/32251293 http://dx.doi.org/10.1038/s41467-020-15340-7 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Lansford, Joshua L.
Vlachos, Dionisios G.
Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
title Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
title_full Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
title_fullStr Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
title_full_unstemmed Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
title_short Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
title_sort infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089992/
https://www.ncbi.nlm.nih.gov/pubmed/32251293
http://dx.doi.org/10.1038/s41467-020-15340-7
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