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An exploration algorithm for stochastic simulators driven by energy gradients

Publication ,  Journal Article
Georgiou, AS; Bello-Rivas, JM; Gear, CW; Wu, HT; Chiavazzo, E; Kevrekidis, IG
Published in: Entropy
July 1, 2017

In recent work, we have illustrated the construction of an exploration geometry on free energy surfaces: the adaptive computer-assisted discovery of an approximate low-dimensional manifold on which the effective dynamics of the system evolves. Constructing such an exploration geometry involves geometry-biased sampling (through both appropriately-initialized unbiased molecular dynamics and through restraining potentials) and, machine learning techniques to organize the intrinsic geometry of the data resulting from the sampling (in particular, diffusion maps, possibly enhanced through the appropriate Mahalanobis-type metric). In this contribution, we detail a method for exploring the conformational space of a stochastic gradient system whose effective free energy surface depends on a smaller number of degrees of freedom than the dimension of the phase space. Our approach comprises two steps. First, we study the local geometry of the free energy landscape using diffusion maps on samples computed through stochastic dynamics. This allows us to automatically identify the relevant coarse variables. Next, we use the information garnered in the previous step to construct a new set of initial conditions for subsequent trajectories. These initial conditions are computed so as to explore the accessible conformational space more efficiently than by continuing the previous, unbiased simulations. We showcase this method on a representative test system.

Duke Scholars

Published In

Entropy

DOI

EISSN

1099-4300

Publication Date

July 1, 2017

Volume

19

Issue

7

Related Subject Headings

  • Fluids & Plasmas
  • 51 Physical sciences
  • 49 Mathematical sciences
  • 02 Physical Sciences
  • 01 Mathematical Sciences
 

Citation

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Georgiou, A. S., Bello-Rivas, J. M., Gear, C. W., Wu, H. T., Chiavazzo, E., & Kevrekidis, I. G. (2017). An exploration algorithm for stochastic simulators driven by energy gradients. Entropy, 19(7). https://doi.org/10.3390/e19070294
Georgiou, A. S., J. M. Bello-Rivas, C. W. Gear, H. T. Wu, E. Chiavazzo, and I. G. Kevrekidis. “An exploration algorithm for stochastic simulators driven by energy gradients.” Entropy 19, no. 7 (July 1, 2017). https://doi.org/10.3390/e19070294.
Georgiou AS, Bello-Rivas JM, Gear CW, Wu HT, Chiavazzo E, Kevrekidis IG. An exploration algorithm for stochastic simulators driven by energy gradients. Entropy. 2017 Jul 1;19(7).
Georgiou, A. S., et al. “An exploration algorithm for stochastic simulators driven by energy gradients.” Entropy, vol. 19, no. 7, July 2017. Scopus, doi:10.3390/e19070294.
Georgiou AS, Bello-Rivas JM, Gear CW, Wu HT, Chiavazzo E, Kevrekidis IG. An exploration algorithm for stochastic simulators driven by energy gradients. Entropy. 2017 Jul 1;19(7).

Published In

Entropy

DOI

EISSN

1099-4300

Publication Date

July 1, 2017

Volume

19

Issue

7

Related Subject Headings

  • Fluids & Plasmas
  • 51 Physical sciences
  • 49 Mathematical sciences
  • 02 Physical Sciences
  • 01 Mathematical Sciences