2016/12/31 by Eliodoro Chiavazzo, Chiavazzo, Eliodoro, Ronald R. Coifman +11 · 1 citation
Biochemistry, Genetics and Molecular Biology · Materials Science · Physics and Astronomy · #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Protein Structure and Dynamics #Theoretical and Computational Physics
paper · doi:10.48550/arxiv.1701.01513
openalex publication_date 2016/12/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We describe and implement iMapD, a computer-assisted approach for accelerating the exploration of uncharted effective Free Energy Surfaces (FES), and more generally for the extraction of coarse-grained, macroscopic information from atomistic or stochastic (here Molecular Dynamics, MD) simulations. The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator towards new, unexplored phase space regions by exploiting the smoothness of the (gradually, as the exploration progresses) revealed intrinsic low-dimensional geometry of the FES.