Stochastic Surface Walking Method for Structure Prediction and Pathway Searching

We propose an unbiased general-purpose potential energy surface (PES) searching method for both the structure and the pathway prediction of a complex system. The method is based on the idea of bias-potential-driven dynamics and Metropolis Monte Carlo. A central feature of the method is able to perturb smoothly a structural configuration toward a new configuration and simultaneously has the ability to surmount the high barrier in the path. We apply the method for locating the global minimum (GM) of short-ranged Morse clusters up to 103 atoms starting from a random structure without using extra information from the system. In addition to GM searching, the method can identify the pathways for chemical reactions with large dimensionality, as demonstrated in a nanohelix transformation containing 222 degrees of freedoms.