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

Abstract

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.

Authors:
ORCiD logo [1]; ORCiD logo [2]
  1. Univ. of Delaware, Newark, DE (United States). Department of Chemical Biomolecular Engineering
  2. Univ. of Delaware, Newark, DE (United States). Department of Chemical Biomolecular Engineering; Univ. of Delaware, Newark, DE (United States). Catalysis Center for Energy Innovation
Publication Date:
Research Org.:
Energy Frontier Research Centers (EFRC) (United States). Catalysis Center for Energy Innovation (CCEI); Univ. of Delaware, Newark, DE (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1624262
Grant/Contract Number:  
SC0001004
Resource Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
Science & Technology - Other Topics

Citation Formats

Lansford, Joshua L., and Vlachos, Dionisios G. Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials. United States: N. p., 2020. Web. doi:10.1038/s41467-020-15340-7.
Lansford, Joshua L., & Vlachos, Dionisios G. Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials. United States. https://doi.org/10.1038/s41467-020-15340-7
Lansford, Joshua L., and Vlachos, Dionisios G. Mon . "Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials". United States. https://doi.org/10.1038/s41467-020-15340-7. https://www.osti.gov/servlets/purl/1624262.
@article{osti_1624262,
title = {Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials},
author = {Lansford, Joshua L. and Vlachos, Dionisios G.},
abstractNote = {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.},
doi = {10.1038/s41467-020-15340-7},
journal = {Nature Communications},
number = 1,
volume = 11,
place = {United States},
year = {2020},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Fig. 1 Fig. 1: Approach combining expert knowledge and spectroscopic data to close the materials gap. The materials gap (a) and corresponding workflow (b) to address this gap. a Surface structure with adsorbates can be characterized only in controlled and limited environments. Materials used in industrially relevant catalysis have no reliable characterizationmore » method to quantify the structure of the surface in detail. This gap between studies that can be performed with model catalysts and those on complex materials is known as the materials gap. b Develops a workflow to address this gap via physics- and data-driven surrogate models. DFT generates spectra of single CO molecules chemisorbed on different sites of many Pt nanoparticles. After non-local minima (outliers) are removed, a data-driven approach is used to assign labels to each data point that describe structure. The binding-type (atop, bridge, threefold, and fourfold) is naturally discrete and given a numerical class value 1–4. The coordination environment of the site is quantified by its GCN value, which is nearly continuous. K-means clustering is an unsupervised learning method that assigns GCN values to discrete GCN groups necessary for data-driven structure quantification. A physics-driven surrogate model is then applied to expand the filtered primary DFT-based dataset to a secondary dataset of complex spectra. This surrogate model includes coverage scaling factors that quantitatively link shifts in frequencies and intensities of the primary filtered data set to spatial coverage. The coverage scaling is done simultaneously with spectral mixing where individual simple-CO spectra are summed to enforce physics-driven coverage constraints. The last step in the physics-driven surrogate model is spectral convolution accomplished by a Fourier transform to generate synthetic complex spectra with varying line widths. Hundreds of thousands of complex spectra associated with various distributions of occupied sites, with varying coverage and differing line widths, are generated. In generating complex spectra, the binding-type and GCN group of the individual spectra are tallied and used to generate binding-type and GCN probability distribution functions (pdfs) that correspond to each complex spectrum. A data-driven surrogate model is trained on these synthetic complex spectra to learn microstructure. After training on synthetic complex spectra, the model is applied to experimental spectra.« less

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