Computational methodology to determine rare event chemical reaction dynamics and networks

University Of Texas At Austin, Austin TX

Investigators

Abstract

Graeme Henkelman of the University of Texas at Austin is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to address a major fundamental challenge to the field of computational chemistry and materials science. This challenge is to model the dynamics of active materials over experimental time scales. Conventional computational algorithms allow for simulations of atomic scale systems for very short times: from nanoseconds to microseconds. Such simulations are useful, but they are a million times smaller than the timescale needed to study important systems and processes including catalysis and batteries. Henkelman and coworkers aim to bridge this so-called "timescale gap" through the further development and improvement of a method called adaptive kinetic Monte Carlo (AKMC). In AKMC, an efficient exploration of the potential energy surface is used to determine reaction rates and reaction mechanisms. The goal of this project is to overcome the technical and algorithmic challenges related to applying AKMC to the discovery of new catalysts and functional materials. A significant broader impact of this project is the development, distribution and support of a software tool that is freely available to the entire research community. This software permits scientists to directly model their materials over relevant experimental timescales. The efficiency of AKMC relies on transition state theory, where the simulation timescale is determined by the rate of the slow chemical transitions of interest rather than the vibrational timescale of atomic vibrations, which is the limitation for molecular dynamics. While AKMC has been used routinely for model systems based upon empirical potentials - fit to experiment - it needs to be extended to accurate calculations based upon quantum mechanics which can be used to model systems that are relevant to, for example, energy applications. For that, AKMC need to be coupled to standard chemical and materials modeling software, based upon density functional theory, so that the scientists and engineers working to discover new catalysts and improved materials can use this methodology to simulate the timescales of relevance for their applications. A limitation of AKMC and an ongoing challenge that is addressed in this project is the problem of low barriers. An explicit treatment of the state-to-state kinetics can require an intractable number of KMC transitions over low barriers between the higher barrier events of interest. A strategy for overcoming the low barrier problem is to use an analytic solution of the master equation such as the Monte Carlo with adsorbing Markov chains method. In large and disordered systems, however, the number of states connected by low barriers (superbasin states) grows exponentially with system size. Not only is this a problem for the efficient solving of the rate equations, large global superbasins need to be reconstructed whenever an AKMC step alters the superbasin structure. A localization algorithm is being implemented to avoid the combinatorial increase of superbasin states. Development also focuses on a method to automatically build reaction networks of catalytic systems. Specifically, Cu oxidation and catalytic CO oxidation is being modeled on supported metal alloy nanoparticles. The intrinsic activity of these nanoparticles can be a result of direct and dynamic participation of the nanoparticle and support atoms in the reaction mechanisms. In this project, stable states are defined by a clustering algorithm of reactive events from long time scale trajectories. In this way, long time scale dynamics may be used to determine reactivity descriptors for complex catalytic systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.