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 binding on realistic, low-symmetry nanostructures, which is an important step in modeling reaction networks as well as for rational catalyst design in general.

A Critical Review of Machine Learning of Energy Materials

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Machine learning meets volcano plots: computational discovery of cross-coupling catalysts

“…While, generally speaking, applications of machine learning methods in chemistry are still in their infancy, their use has begun to appear in the fields of materials science 48 – 53 and catalysis. 54 – 61 For example, a gradient-boosting regression method 62 has been used to predict the d-band center of mono and bimetallic surfaces 63 and to estimate CO adsorption energies on Pt nanoparticles, 64 while a local similarity kernel could predict the catalytic activity of nanoparticles. 65 Moreover, applications of support vector machines (SVMs) 66 were able to anticipate CO 2 uptake in metal organic frameworks (MOFs) 67 by developing an atomic property-weighted radial distribution function (AP-RDF) based descriptor 68 that captures geometric and chemical features of periodic systems.…”

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

“…Several successful examples have already been reported for organic chemistry reactions, including those that involve homogeneous catalysts . However, the applicability of ML predictions for heterogeneous catalysis have been limited mainly to computationally determined values such as band gaps, d‐band centers, and adsorption energies . For the practical use of ML for discovering new solid catalytic materials, not only first‐principles calculated values but also experimental values for specific catalytic reactions are needed, especially in heterogeneous catalysis because an adequate theoretical model for heterogeneous catalysis is not available.…”