Predicting Adsorption Properties of Catalytic Descriptors on Bimetallic Nanoalloys with Site-Specific Precision

Abstract

Bimetallic nanoparticles present a vastly tunable structural and compositional design space rendering them promising materials for catalytic and energy applications. Yet it remains an enduring challenge to efficiently screen candidate alloys with atomic level specificity while explicitly accounting for their inherent stabilities under reaction conditions. Herein, by leveraging correlations between binding energies of metal adsorption sites and metal–adsorbate complexes, we predict adsorption energies of typical catalytic descriptors (OH*, CH3*, CH*, and CO*) on bimetallic alloys with site-specific resolution. We demonstrate that our approach predicts adsorption energies on top and bridge sites of bimetallic nanoparticles having generic morphologies and chemical environments with errors between 0.09 and 0.18 eV. By forging a link between the inherent stability of an alloy and the adsorption properties of catalytic descriptors, we can now identify active site motifs in nanoalloys that possess targeted catalytic descriptor values while being thermodynamically stable under working conditions.

General screening of surface alloys for catalysis

“…Due to the difficulty of high-throughput screening, significant research has been devoted to developing efficient, parameterized models to predict adsorption energies and hence catalytic performance. [8][9][10][11][12][13][14][15] These models can achieve high accuracy, and have resulted in some successes in screening a particular alloy architecture for a particular reaction. However, past models have generally focused on predicting the adsorption energies of only one or two species, and hence are not applicable to a wide range of reactions.…”

Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals

“…Thus, the screening may be reduced to obtain the thermodynamics for the adsorbed species and couple them with linear-scaling relationships and microkinetic models to simulate operation conditions. The upgrade of biomass-derived molecules is often done by metals and alloys 1,13 , and lately attention has been drawn to the versatile properties of single-atom alloys (SAAs) and near-surface alloys (NSAs) 9,14–22 . The number of combinations is again unlimited and some have shown an almost continuum of adsorption strengths 21 .…”

“…Further linear dependencies have been identified for heteroatoms belonging to the same group in the periodic table, i.e., P* scales with N* 33 . In addition, the accuracy of the above models can be improved by using site-specific adsorption rules 22,32 and the dependence on the local coordination of the adsorption sites 22,34,35 . In the particular case of very small nanoparticles, the activity modulation is linearly dependent with the local electrostatic potential 36 .…”

“…For instance, the formation energies of large molecules in gas phase can be retrieved from neural networks with an accuracy comparable to DFT, 0.04 eV MAE 44 , thus beyond Benson’s model 29 . An alternative approach is to predict the formation energy of few reactivity descriptors from geometric and electronic features, such as atomic radius, local electronegativity, ionic potential, and the coordination of the active site 9,18,22,45 . Particularly, a recent SISSO study considers features from the adsorbate, the metal, and the adsorption site 45 , providing excellent predictions with one DFT evaluation for each metal.…”

Accurate prediction of binding energies for two‐dimensional catalytic materials using machine learning

“…[18][19][20] Machine learning is capable of identifying patterns in complicated multivariate data, and inferring relationships between structural features such as the type of GR defect, type of metal, and a functional property such as the binding energy. [19,[21][22][23][24] It has been successfully used to predict important properties of graphene in the past. [25][26][27] In cases such as these, the success and usefulness of machine learning models is heavily influenced by the choice of features used to describe the material.…”