Graeme Henkelman from the University of Texas at Austin is supported by the Chemical Theory, Models and Computational Methods (CTMC) Program and the Office of Cyber Infrastructure (OCI) in developing algorithms for accelerating molecular dynamics simulations. For rare event systems, such as diffusion in solids and reactions at surfaces, transition state theory (TST) allows for a separation of time scales between that of molecular vibrations and the reactive events of interest. When reactive mechanisms are not known, an adaptive version of the kinetic Monte Carlo (KMC) method is used to find saddle points and rates are evaluated with harmonic TST. To reduce the computational cost, a database is employed to store kinetic events and to make them available for later use in separate calculations with similar chemistry. When the reaction mechanisms for a class of system are known, the computational cost is low because all events are drawn from the database. To further accelerate the simulations, groups of states connected by fast rates are escaped using an analytic solution to the master equation. Storing the local connectivity of these states is essential for efficiency. Accuracy will be improved with a set of methods ranging from harmonic TST to the (classically) exact full TST plus dynamical corrections. The challenge of TST is finding a dividing surface that separates reactants from any product state. A support vector machine is being developed to provide an analytic classification function based upon learned data. Sampled points around the decision surface will be used to train the machine so that it can provide an accurate transition state without prior knowledge of reaction mechanisms.
This work is directed at alleviating the ultralong computational times needed for simulation of realistic chemical or material processes, e.g., catalysis, protein folding, molecular diffusion and so on. The characteristic times for basic molecular motion are many, many times faster than the time scales we measure in the laboratory, and so acceleration algorithms must be developed. EON2 is being developed as a distributed open-source program developed by the PI and collaborators to calculate long-timescale molecular dynamics in systems, for example, undergoing catalys and clustering on metal substrates. It will use advanced database techniques and machine learning, and will be usable in conjunction with other software through customized interfaces. A discussion forum is hosted by the PI for anyone with questions about the algorithms, software or science. The project has been opened up so that computers on campus, supercomputers with idle time, and anyone from the public can contribute computational resources to accelerate dynamics simulations at the atomic scale. This aspect of the outreach program gives the public direct access to the research being done as part of this project.
