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Though "enhanced sampling methods," a class of computational tools, can help accelerate the exploration of the configuration space and dynamics of the system of interest in molecular dynamics (MD) simulations, obtaining accurate thermodynamic and kinetic properties of large systems (>200,000 atoms) from MD simulations in a computationally tractable period is still challenging. To tackle this issue, we developed a novel hybrid enhanced sampling method called parallelized Gaussian accelerated molecular dynamics (ParGaMD) that runs many short Gaussian accelerated molecular dynamics (GaMD) simulations over multiple GPUs in parallel by 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 over multiple GPUs in implemented MD simulation engines. By using the efficient GPU parallelization of the WE framework, we can overcome this bottleneck and additionally sample along the chosen collective variables, which enables ParGaMD to be more powerful than GaMD itself. We show that ParGaMD can significantly speed up sampling different configuration states and dynamics of various systems, which can benefit the broader scientific community.  

 

Also on zoom https://ucdavis.zoom.us/j/98969645841