Reliability Assessment for Large-Scale Molecular Dynamics Approximations
It provides a reliability assessment for large-scale MD approximations, which is important for researchers who need computationally efficient simulations while preserving key physical properties.
This paper constructs a class of large-scale approximations for molecular dynamics simulations and demonstrates numerically that these approximations accurately model macroscopic properties such as energy, linear and angular momenta, despite the chaotic nature of MD.
Molecular dynamics (MD) simulations are used in biochemistry, physics, and other fields to study the motions, thermodynamic properties, and the interactions between molecules. Computational limitations and the complexity of these problems, however, create the need for approximations to the standard MD methods and for uncertainty quantification and reliability assessment of those approximations. In this paper, we exploit the intrinsic two-scale nature of MD to construct a class of large-scale dynamics approximations. The reliability of these methods are evaluated here by measuring the differences between full, classical MD simulations and those based on these large-scale approximations. Molecular dynamics evolutions are non-linear and chaotic, so the complete details of molecular evolutions cannot be accurately predicted even using full, classical MD simulations. This paper provides numerical results that demonstrate the existence of computationally efficient large-scale MD approximations which accurately model certain large-scale properties of the molecules: the energy, the linear and angular momenta, and other macroscopic features of molecular motions.