Abstract
The diverse coordination environments on the surfaces of discrete, three-dimensional (3D) nanoclusters contribute significantly to their unique catalytic properties. Identifying the numerous adsorption sites and diffusion paths on these clusters is however tedious and time-consuming, especially for large, asymmetric nanoclusters. Here, we present a simple, automated method for constructing approximate 2D potential energy surfaces for the adsorption of atomic species on the surfaces of 3D nanoclusters with minimal human intervention. These potential energy surfaces fully characterize the important adsorption sites and diffusion paths on the nanocluster surfaces with accuracies similar to current approaches and at comparable computational cost. Our method can treat complex nanoclusters, such as alloy nanoclusters, and accounts for cluster relaxation and adsorbate-induced reconstruction, important for obtaining accurate energetics. Moreover, its highly parallelizable nature is ideal for modern supercomputer architectures. We showcase our method using two clusters: Au18 and Pt55. For Au18, diffusion of atomic hydrogen between the most stable sites occurs via non-intuitive paths, underlining the necessity of exploring the complete potential energy surface. By enabling the rapid and unbiased assessment of adsorption and diffusion on large, complex nanoclusters, which are particularly difficult to handle manually, our method will help advance materials discovery and the rational design of catalysts.
Highlights
The use of metal nanoclusters in catalytic processes has steadily increased due to their higher dispersion and often times higher intrinsic activities compared with larger metal nanoparticles.[1,2,3,4] This increased catalytic activity is mainly attributed to the presence of stable low-coordination sites,[5,6] other effects, such as quantum-size effects,[7,8] surface-tension-induced strain,[9] and support-metal interactions[10,11] can be relevant
After selecting a well-defined point within the nanocluster, such as its center of mass (CoM), we can parameterize any point on its surface with three spherical coordinates: (i) α, the azimuthal angle (0° ≤ α ≤ 180°); (ii) β, the polar angle (0° ≤ β < 360°); and (iii) R, the radial nanocluster to preserve the integrity of our chosen coordinate system during the constrained optimization of the adsorbate’s position
As fixing atoms reduces the number of degrees of freedom, thereby potentially reducing the computational cost involved and biasing our results in favor with respect to standard manually performed (MP) calculations in terms of two metrics of the Automated Cluster Surface Scanning (ACSS) calculations, we evaluated the npj Computational Materials (2019) 101
Summary
The use of metal nanoclusters in catalytic processes has steadily increased due to their higher dispersion and often times higher intrinsic activities compared with larger metal nanoparticles.[1,2,3,4] This increased catalytic activity is mainly attributed to the presence of stable low-coordination sites,[5,6] other effects, such as quantum-size effects,[7,8] surface-tension-induced strain,[9] and support-metal interactions[10,11] can be relevant. The computational and human workload for manual exploration of small clusters is manageable, comprehensive investigations of larger, more complex nanoclusters (e.g., alloy nanoclusters) requires extensive and tedious human effort In these situations, the possibility of missing potentially important adsorption sites increases rapidly. That these equidistant meshes in (α, β) space are not equidistant in real space and lead to oversampling of real space regions when α is close to 0° or 180°; one may wish to use more complex meshes that can sample real space uniformly to save on computational cost
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