Computational Methods for Simulating Long Time Scales
and for Catalyst Design
A computational method will be presented for simulating the dynamics of atomic systems on time scales much longer than can be accessed with classical molecular dynamics. In this adaptive kinetic Monte Carlo approach, possible reaction mechanisms available to the system are found by exploring the potential energy surface from minima to find nearby saddle points. Reaction rates are then calculated using harmonic transition state theory, and the system is propagated stochastically in time. Our algorithm is sufficiently efficient to model the evolution of systems at the density functional level of theory. I will show a few examples, including surface diffusion and chemical reactions at surfaces. Using the calculated reaction pathways for a catalyst we can then start to design new catalysts computationally. Specifically, I will show recent results from a collaborative effort with an experimental group to design, synthesize and test new nanoparticle catalysts for the oxygen reduction reaction.