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

Authors:
 [1];  [2]; ORCiD logo [3]; ORCiD logo [4]
  1. Department of Material Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States
  2. Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, United States
  3. Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, United States
  4. Department of Material Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States; Division of Chemistry, Brookhaven National Laboratory, Upton, New York 11973, United States
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1399137
Resource Type:
Journal Article
Journal Name:
Journal of Physical Chemistry Letters
Additional Journal Information:
Journal Volume: 2017; Journal Issue: (8) ; 09, 2017; Journal ID: ISSN 1948-7185
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
ENGLISH

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.
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.
Timoshenko, Janis, Lu, Deyu, Lin, Yuewei, and Frenkel, Anatoly I. Wed . "Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles". United States. doi:10.1021/acs.jpclett.7b02364.
@article{osti_1399137,
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 = {},
doi = {10.1021/acs.jpclett.7b02364},
journal = {Journal of Physical Chemistry Letters},
issn = {1948-7185},
number = (8) ; 09, 2017,
volume = 2017,
place = {United States},
year = {2017},
month = {10}
}

Works referencing / citing this record:

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

  • Miyazato, Itsuki; Takahashi, Lauren; Takahashi, Keisuke
  • Molecular Systems Design & Engineering, Vol. 4, Issue 5
  • DOI: 10.1039/c9me00043g

Formation and Functioning of Bimetallic Nanocatalysts: The Power of X‐ray Probes
journal, July 2019

  • Filez, Matthias; Redekop, Evgeniy A.; Dendooven, Jolien
  • Angewandte Chemie, Vol. 131, Issue 38
  • DOI: 10.1002/ange.201902859

Formation and Functioning of Bimetallic Nanocatalysts: The Power of X-ray Probes
journal, July 2019

  • Filez, Matthias; Redekop, Evgeniy A.; Dendooven, Jolien
  • Angewandte Chemie International Edition, Vol. 58, Issue 38
  • DOI: 10.1002/anie.201902859