Enhanced fast inertial relaxation engine (FIRE) for multiscale simulations

In multiscale modeling methods (MMM), the integration of atomistic to continuum coupling is a common practice, where two regions have their own distinct length scales. The equilibrium configurations of such multiscale systems under given conditions are typically obtained through energy minimization algorithms (EMA). However, traditional EMAs, such as the conjugate gradient (CG) and limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithms, are unable to discern the diverse scales inherent in such systems. In this work, it is found that the convergence rate of energy minimization in multiscale simulations is significantly slower than that in full atomistic simulations, regardless of using CG, LBFGS algorithms or the latest fast inertial relaxation engine (FIRE). The lower efficiency emerges due to the coexistence of atoms and nodes with distinct length scales within the multiscale framework, yet the current EMAs fail to differentiate between them. It results in disparate convergence rates across different scales, which undermines both computational accuracy and efficiency. To address the issue, a multiscale FIRE algorithm which updates positions of atoms and nodes synchronously by employing appropriate effective mass is proposed. The optimal effective mass is determined by synchronizing the vibration of harmonic oscillators across different scales. By employing the multiscale FIRE algorithm, the computational efficiency increased by 24.4 and 23.7 times compared to the CG and LBFGS algorithms when used for multiscale nanoindentation simulations. These findings and the proposed algorithm provide valuable insights for structural relaxations of multiscale physical problems and are promising to further improve the computational accuracy and efficiency of MMMs.