Alloy Catalyst Discovery Using Computational Alchemy

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.

Reference:

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).