Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

doi:10.1021/acs.jpclett.7b02364

Title: Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

Abstract

Tracking the structure of heterogeneous catalysts under operando conditions remains a challenge due to the paucity of experimental techniques that can provide atomic-level information for catalytic metal species. Here we report on the use of X-ray absorption near edge structure (XANES) spectroscopy and supervised machine learning (SML) for refining the three-dimensional geometry of metal catalysts. SML is used to unravel the hidden relationship between the XANES features and catalyst geometry. To train our SML method, we rely on ab-initio XANES simulations. Our approach allows one to solve the structure of a metal catalyst from its experimental XANES, as demonstrated here by reconstructing the average size, shape and morphology of well-defined platinum nanoparticles. This method is applicable to the determination of the nanoparticle structure in operando studies and can be generalized to other nanoscale systems. In conclusion, it also allows on-the-fly XANES analysis, and is a promising approach for high-throughput and time-dependent studies.

Authors:

Timoshenko, Janis ^[1]; Lu, Deyu ^[2];

^[2];

^[3]

Stony Brook Univ., Stony Brook, NY (United States)
Brookhaven National Lab. (BNL), Upton, NY (United States)
Stony Brook Univ., Stony Brook, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)

Publication Date:: 2017-09-29

Research Org.:: Stony Brook Univ., Stony Brook, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

OSTI Identifier:: 1395675

Alternate Identifier(s):: OSTI ID: 1425047

Report Number(s):: BNL-114511-2017-JAAM
Journal ID: ISSN 1948-7185

Grant/Contract Number:: FG02-03ER15476; SC0012704

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Physical Chemistry Letters

Additional Journal Information:: Journal Volume: 8; Journal Issue: 20; Journal ID: ISSN 1948-7185

Publisher:: American Chemical Society

Country of Publication:: United States

Language:: English

Subject:: 36 MATERIALS SCIENCE; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; neural networks; nanoparticles; X-ray absorption spectroscopy; coordination numbers; three-dimensional structure; 37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY

Citation Formats


                    Timoshenko, Janis, Lu, Deyu, Lin, Yuewei, and Frenkel, Anatoly I. Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles.  United States: N. p., 2017. 
        Web.  doi:10.1021/acs.jpclett.7b02364.

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                    Timoshenko, Janis, Lu, Deyu, Lin, Yuewei, & Frenkel, Anatoly I. Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles.  United States.  doi:10.1021/acs.jpclett.7b02364.

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                    Timoshenko, Janis, Lu, Deyu, Lin, Yuewei, and Frenkel, Anatoly I. Fri .  
        "Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles".  United States.  doi:10.1021/acs.jpclett.7b02364.  https://www.osti.gov/servlets/purl/1395675.

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@article{osti_1395675,

  title        = {Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles},

  author       = {Timoshenko, Janis and Lu, Deyu and Lin, Yuewei and Frenkel, Anatoly I.},

  abstractNote = {Tracking the structure of heterogeneous catalysts under operando conditions remains a challenge due to the paucity of experimental techniques that can provide atomic-level information for catalytic metal species. Here we report on the use of X-ray absorption near edge structure (XANES) spectroscopy and supervised machine learning (SML) for refining the three-dimensional geometry of metal catalysts. SML is used to unravel the hidden relationship between the XANES features and catalyst geometry. To train our SML method, we rely on ab-initio XANES simulations. Our approach allows one to solve the structure of a metal catalyst from its experimental XANES, as demonstrated here by reconstructing the average size, shape and morphology of well-defined platinum nanoparticles. This method is applicable to the determination of the nanoparticle structure in operando studies and can be generalized to other nanoscale systems. In conclusion, it also allows on-the-fly XANES analysis, and is a promising approach for high-throughput and time-dependent studies.},

  doi          = {10.1021/acs.jpclett.7b02364},

  journal      = {Journal of Physical Chemistry Letters},

  number       = 20,

  volume       = 8,

  place        = {United States},

  year         = {2017},

  month        = {9}

}

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DOI: 10.1021/acs.jpclett.7b02364

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Title: Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

Abstract

Citation Formats

Automatic oxidation threshold recognition of XAFS data using supervised machine learning journal, January 2019

Automatic oxidation threshold recognition of XAFS data using supervised machine learning
journal, January 2019