Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model
This addresses a bottleneck in molecular dynamics for researchers studying complex systems, offering an incremental improvement in unbiasing techniques.
The paper tackled the challenge of unbiasing enhanced sampling simulations with high-dimensional collective variables, which is difficult for traditional methods, by proposing a score-based diffusion model approach. The results show it significantly outperforms traditional unbiasing methods and accurately generates unbiased conformational ensembles for simulations with more CVs than usual.
Biased enhanced sampling methods utilizing collective variables (CVs) are powerful tools for sampling conformational ensembles. Due to high intrinsic dimensions, efficiently generating conformational ensembles for complex systems requires enhanced sampling on high-dimensional free energy surfaces. While methods like temperature-accelerated molecular dynamics (TAMD) can adopt many CVs in a simulation, unbiasing the simulation requires accurate modeling of a high-dimensional CV probability distribution, which is challenging for traditional density estimation techniques. Here we propose an unbiasing method based on the score-based diffusion model, a deep generative learning method that excels in density estimation across complex data landscapes. We test the score-based diffusion unbiasing method on TAMD simulations. The results demonstrate that this unbiasing approach significantly outperforms traditional unbiasing methods, and can generate accurate unbiased conformational ensembles for simulations with a number of CVs higher than usual ranges.