(544at) Prediction of Surface Energies for Complex Pt Structures from Coordination Number and Generalized Coordination Number

Authors: 
Zhong, W., Carnegie Mellon University
Hanselman, C. L., Carnegie Mellon University
Tran, K., Carnegie Mellon University
Gounaris, C. E., Carnegie Mellon University
Ulissi, Z., Carnegie Mellon University

Prediction of Surface
Energies for Complex Pt Structures from Coordination Number and Generalized Coordination
Number

Wen Zhong, Christopher
L. Hanselman, Kevin Tran, Chrysanthos E. Gounaris, Zachary W. Ulissi

Transition metal nanoparticles can show enhanced catalytic
activity over single surfaces. Nanoparticle site activity has been shown to be
correlated with simple descriptors like the generalized coordination number [1]
. Textured or patterned surfaces can be formulated as an optimization problem
and can show even higher activities but the stability of these surfaces needs
to be considered [2].
We use simple regressions to directly predict the surface stability from the
same generalized coordination number descriptor, allowing stability to be added
to the optimization problem. We compare with 106 explicit DFT surface energy calculations
for patterned surfaces, achieving an RMSE of 0.0023 eV/A^2 and 0.0020 eV/A^2
for coordination number and generalized coordination number descriptors
respectively. We show that this model can also apply to larger and more
complicated surfaces not in the training set. We also find that 80% of the
patterned structures have surface energies that are greater than typical
high-energy nanoparticle facets. We use these predictions to identify a number
of pareto-optimal surfaces with both high activity and stability.

References

[1]       F. Calle-Vallejo, J. I. Martínez, J. M.
García-Lastra, P. Sautet, and D. Loffreda, “Fast prediction of adsorption
properties for platinum nanocatalysts with generalized coordination numbers,” Angew.
Chemie - Int. Ed.
, vol. 53, no. 32, pp. 8316–8319, 2014.

[2]       C. L. Hanselman and C. E. Gounaris, “A
Mathematical Optimization Framework for the Design of Nanopatterned Surfaces
Christopher,” AIChE J., vol. 62, no. 9, pp. 3250–3263, 2016.  

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