Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models
Abstract
We present a systematic analysis of CO adsorption on Pt nanoclusters in the 0.2–1.5 nm size range with the aim of unraveling size-dependent trends and developing predictive models for site-specific adsorption behavior. Using an empirical-potential-based genetic algorithm and density functional theory (DFT) modeling, we show that there exists a size window (40–70 atoms) over which Pt nanoclusters bind CO weakly, the binding energies being comparable to those on (111) or (100) facets. The size-dependent adsorption energy trends are, however, distinctly nonmonotonic and are not readily captured using traditional descriptors such as d-band energies or (generalized) coordination numbers of the Pt binding sites. Instead, by applying machine-learning algorithms, we show that multiple descriptors, broadly categorized as structural and electronic descriptors, are essential for qualitatively capturing the CO adsorption trends. Nevertheless, attaining quantitative accuracy requires further refinement, and we propose the use of an additional descriptors—the fully frozen adsorption energy—that is a computationally inexpensive probe of CO–Pt bond formation. With these three categories of descriptors, we achieve an absolute mean error in CO adsorption energy prediction of 0.12 eV, which is similar to the underlying error of DFT adsorption calculations. Our approach allows for building quantitatively predictive models of site-specific adsorbate bindingmore »
- Authors:
-
- Univ. of Massachusetts, Amherst, MA (United States). Dept. of Chemical Engineering
- Univ. of Massachusetts, Amherst, MA (United States). Dept. of Mechanical and Industrial Engineering
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1480455
- Grant/Contract Number:
- SC0010610; AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Physical Chemistry. C
- Additional Journal Information:
- Journal Volume: 121; Journal Issue: 10; Journal ID: ISSN 1932-7447
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; 42 ENGINEERING; 77 NANOSCIENCE AND NANOTECHNOLOGY
Citation Formats
Gasper, Raymond, Shi, Hongbo, and Ramasubramaniam, Ashwin. Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models. United States: N. p., 2017.
Web. doi:10.1021/acs.jpcc.6b12800.
Gasper, Raymond, Shi, Hongbo, & Ramasubramaniam, Ashwin. Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models. United States. https://doi.org/10.1021/acs.jpcc.6b12800
Gasper, Raymond, Shi, Hongbo, and Ramasubramaniam, Ashwin. Mon .
"Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models". United States. https://doi.org/10.1021/acs.jpcc.6b12800. https://www.osti.gov/servlets/purl/1480455.
@article{osti_1480455,
title = {Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models},
author = {Gasper, Raymond and Shi, Hongbo and Ramasubramaniam, Ashwin},
abstractNote = {We present a systematic analysis of CO adsorption on Pt nanoclusters in the 0.2–1.5 nm size range with the aim of unraveling size-dependent trends and developing predictive models for site-specific adsorption behavior. Using an empirical-potential-based genetic algorithm and density functional theory (DFT) modeling, we show that there exists a size window (40–70 atoms) over which Pt nanoclusters bind CO weakly, the binding energies being comparable to those on (111) or (100) facets. The size-dependent adsorption energy trends are, however, distinctly nonmonotonic and are not readily captured using traditional descriptors such as d-band energies or (generalized) coordination numbers of the Pt binding sites. Instead, by applying machine-learning algorithms, we show that multiple descriptors, broadly categorized as structural and electronic descriptors, are essential for qualitatively capturing the CO adsorption trends. Nevertheless, attaining quantitative accuracy requires further refinement, and we propose the use of an additional descriptors—the fully frozen adsorption energy—that is a computationally inexpensive probe of CO–Pt bond formation. With these three categories of descriptors, we achieve an absolute mean error in CO adsorption energy prediction of 0.12 eV, which is similar to the underlying error of DFT adsorption calculations. Our approach allows for building quantitatively predictive models of site-specific adsorbate binding on realistic, low-symmetry nanostructures, which is an important step in modeling reaction networks as well as for rational catalyst design in general.},
doi = {10.1021/acs.jpcc.6b12800},
journal = {Journal of Physical Chemistry. C},
number = 10,
volume = 121,
place = {United States},
year = {Mon Feb 20 00:00:00 EST 2017},
month = {Mon Feb 20 00:00:00 EST 2017}
}
Web of Science
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