Accelerating Free Energy Exploration Using Parallelizable Gaussian Accelerated Molecular Dynamics (ParGaMD)

Though "enhanced sampling methods," a class of computational tools, can help accelerate the exploration of the system's configuration space and dynamics in molecular dynamics (MD) simulations, obtaining accurate thermodynamic and kinetic properties of large systems within a computationally tractable period remains challenging. To tackle this issue, we developed a novel hybrid enhanced sampling method, parallelizable Gaussian accelerated molecular dynamics (ParGaMD), that runs many short Gaussian accelerated molecular dynamics (GaMD) simulations across multiple GPUs in parallel using the weighted ensemble method (WE) framework. Although GaMD accelerates sampling by adding a harmonic boost potential to the system, GaMD often takes weeks to run for large systems and does not parallelize well across multiple GPUs in the implemented MD simulation engines. By leveraging the WE framework's efficient GPU parallelization, we can overcome this bottleneck and additionally sample along the chosen collective variables, thereby making ParGaMD more powerful than GaMD itself. We show that ParGaMD can significantly accelerate sampling of different configuration states and dynamics across various systems, benefiting the broader scientific community.