| The present study proposes the use of principal component analysis as a robust method of reducing the effective dimensionality of a multivariate time-series dataset, to analyze the underlying kinetics of a process studied by molecular dynamics simulations. As a proof of concept, this technique has been applied to explore the mechanism of neck formation during the sintering of two gold nanoparticles. It is found that the method of dimensionality reduction provides an intuitive and simple one-dimensional representation of this complex process, using which the quantities like rate of the process, thermodynamic forces and effective inertia can be evaluated. Both isothermal sintering and linear heating have been simulated and the fundamental differences between the kinetics of these two cases have been highlighted. |
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