Stochastic Surface Walking Method and Applications to Real Materials
The macroscopic properties of a material are generally governed by its potential energy surface (PES) that determines not only thermodynamics but also kinetics. A thoroughly search of the global PES of material, however, has been a great challenge in theory. Three major hurdles, namely, the high energy barrier in material transformation, the large entropy due to huge structural configurations, and the large atomic degrees of freedom, are often encountered simultaneously in computational simulation of material. Owing to these, the limitation in the timescale of current simulations restricts heavily theoreticians to address many important questions in material science. In this chapter, we introduce a newly developed theoretical method, stochastic surface walking (SSW) method targeting for both global PES exploration and reaction pathway sampling. The SSW PES sampling is automated, unbiased, and taking into account the second derivative information. The algorithm of SSW is summarized here in detail, focusing on its mechanism to follow low energy pathways while being able to overcome high barriers. SSW simulation has recently been applied to different areas in material and reaction systems. Several typical examples of PES exploration by combining SSW with first principles and neural network potential calculations are presented to illustrate the power of SSW for unbiased PES exploration and pathway searching.
This authors acknowledge National Science Foundation of China (21603035, 21533001), Science and Technology Commission of Shanghai Municipality (08DZ2270500), Shanghai Pujiang Program (16PJ1401200) for financial supports.
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