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Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models
journal contribution
posted on 2017-02-20, 00:00 authored by Raymond Gasper, Hongbo Shi, Ashwin RamasubramaniamWe
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 descriptorsthe fully frozen
adsorption energythat 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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Keywords
machine-learning algorithmsCO adsorptionMachine-Learning Models0.12 eVLow-Symmetry Pt NanoparticlesPt nanoclusterssize-dependent adsorption energy trendsCO adsorption energy predictionbinding energiesDFT adsorption calculationsunraveling size-dependent trendslow-symmetry nanostructuresEnergy Decomposition AnalysisCO adsorption trendscatalyst designPt nanoclusters bind COband energiesdescriptorcoordination numberssite-specific adsorption behaviorPt binding sitesmodeling reaction networkssite-specific adsorbate binding