jp6b12800_si_001.pdf (1.62 MB)
Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models
journal contributionposted on 2017-02-20, 00:00 authored by Raymond Gasper, Hongbo Shi, Ashwin Ramasubramaniam
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 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.
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