(777i) Alloy Catalyst Discovery Using Computational Alchemy | AIChE

(777i) Alloy Catalyst Discovery Using Computational Alchemy


Saravanan, K. - Presenter, University of Pittsburgh
Keith, J., University of Pittsburgh
von Lilienfeld, O. A., Sandia National Laboratories

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).