468893 Adaptive Coarse-Graining of Molecular Dynamics through Diffusion Maps
When this advance knowledge is not available, we couple equation-free methods to diffusion maps, a nonlinear manifold learning algorithm, to learn the coarse variables “on the fly” based on relatively short bursts of the MD simulator. By establishing the low dimensionality of an attracting manifold and geometrically extrapolating outwards, the simulator can be biased towards rare events, effectively moving up potential gradients and out of potential wells. This allows the simulator to time-effectively search the potential landscape for new stable states and transition pathways. We demonstrate our approach on a toy stochastic simulator as well as MD simulations of alanine dipeptide.
See more of this Group/Topical: Computational Molecular Science and Engineering Forum