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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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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. |
format | Online Article Text |
id | pubmed-7089992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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