Algorithms for adaptively restrained molecular dynamics

Molecular Dynamics (MD) is often used to simulate large and complex systems. Although, simulating such complex systems for the experimental time scales are still computationally challenging. In fact, the most computationally extensive step in MD is the computation of forces between particles. Adaptively Restrained Molecular Dynamics (ARMD) is a recently introduced particles simulation method that switches positional degrees of freedom on and off during simulation. Since force computations mainly depend upon the inter-atomic distances, the force computation between particles with positional degrees of freedom off~(restrained particles) can be avoided. Forces involving active particles (particles with positional degrees of freedom on) are computed.In order to take advantage of adaptability of ARMD, we designed novel algorithms to compute and update forces efficiently. We designed algorithms not only to construct neighbor lists, but also to update them incrementally. Additionally, we designed single-pass incremental force update algorithm that is almost two times faster than previously designed two-pass incremental algorithm. These proposed algorithms are implemented and validated in the LAMMPS MD simulator, however, these algorithms can be applied to other MD simulators. We assessed our algorithms on different and diverse benchmarks in both microcanonical ensemble (NVE) and canonical (NVT) ensembles. In the NVE ensemble, ARMD allows users to trade between precision and speed while, in the NVT ensemble, it makes it possible to compute statistical averages faster. In Last, we introduce parallel algorithms for single-pass incremental force computations to take advantage of adaptive restraints using the Message Passage Interface (MPI) standard.