2017 Annual Meeting

(684c) Accelerated Catalyst Screening Using Computational Alchemy

Authors

Karthikeyan Saravanan - Presenter, University of Pittsburgh
John Keith, University of Pittsburgh
John Kitchin, Carnegie Mellon University
O. Anatole von Lilienfeld, Sandia National Laboratories
Large number of screening studies identifying new catalysts for different reactions have been reported over the past decade. Almost all of them employ Kohn-Sham density functional theory (KS-DFT) and thermodynamic descriptors (see for example ref. [1]) to screen for new catalysts. Though usually considered reliable for descriptor-based analyses, KS-DFT calculations are computationally expensive and intractable for use when screening across the full chemical space of all possible alloy materials. In order to accelerate screening of catalysts, we employ a model Hamiltonian method, ‘computational alchemy’ [2-4] to approximate KS-DFT energies at a fraction of the computational cost. We will discuss about computational alchemy and how it can be used to reliably screen for oxygen reduction reaction (ORR) catalysts using Volcano plot descriptors.

Reference:

1. Greeley, J. et al. Alloys of platinum and early transition metals as oxygen reduction electrocatalysts. Nat Chem 1, 552–556 (2009).

2. Lilienfeld, O. A. von, Lins, R. D. & Rothlisberger, U. Variational Particle Number Approach for Rational Compound Design. Phys. Rev. Lett. 95, 153002 (2005).

3. Lilienfeld, O. A. von & Tuckerman, M. E. Molecular grand-canonical ensemble density functional theory and exploration of chemical space. The Journal of Chemical Physics 125, 154104 (2006).

4. Sheppard, D., Henkelman, G. & Lilienfeld, O. A. von. Alchemical derivatives of reaction energetics. The Journal of Chemical Physics 133, 084104 (2010).