Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models

Gasper, Raymond; Shi, Hongbo; Ramasubramaniam, Ashwin

doi:10.1021/acs.jpcc.6b12800

Title: Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models

Journal Article · Mon Feb 20 00:00:00 EST 2017 · Journal of Physical Chemistry. C

DOI:https://doi.org/10.1021/acs.jpcc.6b12800· OSTI ID:1480455

Gasper, Raymond ^[1]; Shi, Hongbo ^[1];

^[2]

Univ. of Massachusetts, Amherst, MA (United States). Dept. of Chemical Engineering
Univ. of Massachusetts, Amherst, MA (United States). Dept. of Mechanical and Industrial Engineering

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0010610; AC02-05CH11231

OSTI ID:: 1480455

Journal Information:: Journal of Physical Chemistry. C, Vol. 121, Issue 10; ISSN 1932-7447

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 48 works

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