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arXiv:2403.19387v1 Announce Type: new
Abstract: Automated molecular simulations are used extensively for predicting material properties. Typically, these simulations exhibit two regimes: a dynamic equilibration part, followed by a steady state. For extracting observable properties, the simulations must first reach a steady state so that thermodynamic averages can be taken. However, as equilibration depends on simulation conditions, predicting the optimal number of simulation steps a priori is impossible. Here, we demonstrate the application of the Marginal Standard Error Rule (MSER) for automatically identifying the optimal truncation point in Grand Canonical Monte Carlo (GCMC) simulations. This novel automatic procedure determines the point in which steady state is reached, ensuring that figures-of-merits are extracted in an objective, accurate, and reproducible fashion. In the case of GCMC simulations of gas adsorption in metal-organic frameworks, we find that this methodology reduces the computational cost by up to 90%. As MSER statistics are independent of the simulation method that creates the data, this library is, in principle, applicable to any time series analysis in which equilibration truncation is required. The open-source Python implementation of our method, pyMSER, is publicly available for reuse and validation at https://github.com/IBM/pymser.