iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2016
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912616041938944 |
|---|---|
| author | Chiavazzo, Eliodoro Coifman, Ronald R. Covino, Roberto Gear, C. William Georgiou, Anastasia S. Hummer, Gerhard Kevrekidis, Ioannis G. |
| author_facet | Chiavazzo, Eliodoro Coifman, Ronald R. Covino, Roberto Gear, C. William Georgiou, Anastasia S. Hummer, Gerhard Kevrekidis, Ioannis G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1701_01513 |
| institution | arXiv |
| publishDate | 2016 |
| record_format | arxiv |
| spellingShingle | iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes Chiavazzo, Eliodoro Coifman, Ronald R. Covino, Roberto Gear, C. William Georgiou, Anastasia S. Hummer, Gerhard Kevrekidis, Ioannis G. Chemical Physics Computational Physics 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. |
| title | iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes |
| topic | Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/1701.01513 |