scholarly journals A replica exchange transition interface sampling method with multiple interface sets for investigating networks of rare events

2014 ◽  
Vol 141 (4) ◽  
pp. 044101 ◽  
Author(s):  
David W. H. Swenson ◽  
Peter G. Bolhuis
Keyword(s):  
Sampling Method ◽  
Rare Events ◽  
Replica Exchange ◽  
Multiple Interface ◽  
Transition Interface

An improved replica-exchange sampling method: Temperature intervals with global energy reassignment

2007 ◽  
Vol 127 (16) ◽  
pp. 164116 ◽  
Author(s):  
Xianfeng Li ◽  
Christopher P. O’Brien ◽  
Galen Collier ◽  
Nadeem A. Vellore ◽  
Feng Wang ◽  
...  
Keyword(s):  
Sampling Method ◽  
Replica Exchange ◽  
Global Energy

scholarly journals Deep learning the slow modes for rare events sampling

2021 ◽  
Vol 118 (44) ◽  
pp. e2113533118
Author(s):  
Luigi Bonati ◽  
GiovanniMaria Piccini ◽  
Michele Parrinello
Keyword(s):  
Sampling Method ◽  
Rare Events ◽  
Conformational Transition ◽  
Transfer Operator ◽  
Atomistic Simulations ◽  
Enhanced Sampling ◽  
Collective Variables ◽  
Long Time ◽  
Computational Resources

The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an appropriate set of collective variables. These are meant to describe the system’s modes that most slowly approach equilibrium under the action of the sampling algorithm. Once identified, the equilibration of these modes is accelerated by the enhanced sampling method of choice. An attractive way of determining the collective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator. Unfortunately, this requires knowing the long-term dynamics of the system beforehand, which is generally not available. However, we have recently shown that it is indeed possible to determine efficient collective variables starting from biased simulations. In this paper, we bring the power of machine learning and the efficiency of the recently developed on the fly probability-enhanced sampling method to bear on this approach. The result is a powerful and robust algorithm that, given an initial enhanced sampling simulation performed with trial collective variables or generalized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then accelerates them to promote sampling of rare events. To illustrate the generality of this approach, we apply it to several systems, ranging from the conformational transition of a small molecule to the folding of a miniprotein and the study of materials crystallization.

scholarly journals Exploring Protocols to Build Reservoirs to Accelerate Replica Exchange MD Simulations

2020 ◽  
Author(s):  
koushik kasavajhala ◽  
kenneth lam ◽  
Carlos Simmerling
Keyword(s):  
Amino Acids ◽  
Sampling Method ◽  
Convergence Rates ◽  
Md Simulations ◽  
Replica Exchange ◽  
Conformational Sampling ◽  
Enhanced Sampling ◽  
Replica Exchange Molecular Dynamics ◽  
The Past ◽  
Biomolecular Simulations

Replica Exchange Molecular Dynamics (REMD) is a widely used enhanced sampling method for accelerating biomolecular simulations. During the past two decades, several variants of REMD have been developed to further improve the rate of conformational sampling of REMD. One such variant, Reservoir REMD (RREMD), was shown to improve the rate of conformational sampling by around 5-20x. Despite the significant increase in sampling speed, RREMD methods have not been widely used due to the difficulties in building the reservoir and also due to the code not being available on the GPUs.<br><br>In this work, we ported the AMBER RREMD code onto GPUs making it 20x faster than the CPU code. Then, we explored protocols for building Boltzmann-weighted reservoirs as well as non-Boltzmann reservoirs, and tested how each choice affects the accuracy of the resulting RREMD simulations. We show that, using the recommended protocols outlined here, RREMD simulations can accurately reproduce Boltzmann-weighted ensembles obtained by much more expensive conventional REMD simulations, with at least 15x faster convergence rates even for larger proteins (>50 amino acids) compared to conventional REMD.

scholarly journals Foundations and latest advances in replica exchange transition interface sampling

2017 ◽  
Vol 147 (15) ◽  
pp. 152722 ◽  
Author(s):  
Raffaela Cabriolu ◽  
Kristin M. Skjelbred Refsnes ◽  
Peter G. Bolhuis ◽  
Titus S. van Erp
Keyword(s):  
Replica Exchange ◽  
Transition Interface

scholarly journals Collapse of transitional wall turbulence captured using a rare events algorithm

2021 ◽  
Vol 931 ◽  
Author(s):  
Joran Rolland
Keyword(s):  
Reynolds Number ◽  
Sampling Method ◽  
Rare Events ◽  
Streamwise Velocity ◽  
Wall Turbulence ◽  
Spanwise Direction ◽  
Streamwise Vortices ◽  
Large Size ◽  
Reactive Trajectories ◽  
O 10

This text presents one of the first successful applications of a rare events sampling method for the study of multistability in a turbulent flow without stochastic energy injection. The trajectories of collapse of turbulence in plane Couette flow, and their probability and rate of occurrence are systematically computed using adaptive multilevel splitting (AMS). The AMS computations are performed in a system of size $L_x\times L_z=24\times 18$ at Reynolds number $R=370$ with an acceleration by a factor ${O}(10)$ with respect to direct numerical simulations (DNS) and in a system of size $L_x\times L_z=36\times 27$ at Reynolds number $R=377$ with an acceleration by a factor ${O}(10^3)$ . The AMS results are validated by a comparison with DNS in the smaller system. Visualisations indicate that turbulence collapses because the self-sustaining process of turbulence fails locally. The streamwise vortices decay first in streamwise elongated holes, leaving streamwise invariant streamwise velocity tubes that experience viscous decay. These holes then extend in the spanwise direction. The examination of more than a thousand trajectories in the $(E_{k,x}=\int u_x^2/2\,\textrm {d}^3\boldsymbol {x},E_{k,y-z}=\int (u_y^2/2+u_z^2/2)\,\textrm {d}^3\boldsymbol {x})$ plane in the smaller system confirms the faster decay of streamwise vortices and shows concentration of trajectories. This hints at an instanton phenomenology in the large size limit. The computation of turning point states, beyond which laminarisation is certain, confirms the hole formation scenario and shows that it is more pronounced in larger systems. Finally, the examination of non-reactive trajectories indicates that both the vortices and the streaks reform concomitantly when the laminar holes close.

scholarly journals Investigating rare events by transition interface sampling

2004 ◽  
Vol 340 (1-3) ◽  
pp. 395-401 ◽  
Author(s):  
Daniele Moroni ◽  
Titus S. van Erp ◽  
Peter G. Bolhuis
Keyword(s):  
Rare Events ◽  
Transition Interface

scholarly journals OpenPathSampling: A Python framework for path sampling simulations. II. Building and customizing path ensembles and sample schemes

2018 ◽  
Author(s):  
David W.H. Swenson ◽  
Jan-Hendrik Prinz ◽  
Frank Noe ◽  
John D. Chodera ◽  
Peter G. Bolhuis
Keyword(s):  
Companion Paper ◽  
Replica Exchange ◽  
Path Sampling ◽  
Transition Path ◽  
Transition Path Sampling ◽  
Molecular Systems ◽  
Sampling Algorithms ◽  
Basic Concepts ◽  
Transition Interface ◽  
Theoretical Developments

The OpenPathSampling (OPS) package provides an easy-to-use framework to apply transition path sampling methodologies to complex molecular systems with a minimum of effort. Yet, the extensibility of OPS allows for the exploration of new path sampling algorithms by building on a variety of basic operations. In a companion paper [Swenson et al 2018] we introduced the basic concepts and the structure of the OPS package, and how it can be employed to perform standard transition path sampling and (replica exchange) transition interface sampling. In this paper, we elaborate on two theoretical developments that went into the design of OPS. The first development relates to the construction of path ensembles, the what is being sampled. We introduce a novel set-based notation forthepath ensemble, which provides an alternative paradigm for constructing path ensembles, and allows building arbitrarily complex path ensembles from fundamental ones. The second fundamental development is the structure for the customisation of Monte Carlo procedures; how path ensembles are being sampled. We describe in detail the OPS objects that implement this approach to customization, the MoveScheme and the PathMover, and provide tools to create and manipulate these objects. We illustrate both the path ensemble building and sampling scheme customization with several examples. OPS thus facilitates both standard path sampling application in complex systems as well as the development of new path sampling methodology, beyond the default.

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