464732 Alloy Catalyst Discovery Using Computational Alchemy

Friday, November 18, 2016: 2:24 PM
Yosemite A (Hilton San Francisco Union Square)
Karthikeyan Saravanan1, O. Anatole von Lilienfeld2 and John A. Keith1, (1)Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, (2)Department of Chemistry, University of Basel, Basel, Switzerland

There is great interest in finding identifying high-performance catalysts that are economical. Computational quantum chemistry schemes employing Kohn-Sham density functional theory (KS-DFT) can be used to screen catalyst materials on the basis of thermodynamic descriptors (see for example ref. [1]). 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. Toward this goal, we employ a model Hamiltonian method, Ôcomputational alchemyÕ [2-4] to approximate KS-DFT energies at a fraction of the computational cost. We will introduce the theory of computational alchemy and how it can be used to facilitate descriptor-based screening on many thousands of alloys structures.


1. Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I. & N¿rskov, J. K. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Nat Mater 5, 909Ð913 (2006).

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).


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