@article{200161,
  keywords = {Model reduction, Protein Folding, machine learning, intrinsic map dynamics exploration uncharted free energy landscape algorithm, enhanced sampling methods, free-energy surface},
  author = {Eliodoro Chiavazzo and Roberto Covino and Ronald R. Coifman and C. William Gear and Anastasia S. Georgiou and Gerhard Hummer and IoannisG. Kevrekidis},
  title = {Intrinsic map dynamics exploration for uncharted effective free-energy landscapes},
  abstract = { We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as mol. dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES. },
  year = {2017},
  journal = {Proc. Natl. Acad. Sci. U. S. A.},
  volume = {114},
  number = {28},
  pages = {E5494},
  publisher = {National Academy of Sciences},
  isbn = {1091-64900027-8424},
  url = {https://doi.org/10.1073/pnas.1621481114},
  language = {eng},
}