BAR-based optimum adaptive sampling regime for variance minimization in alchemical transformation: the nonequilibrium stratification

2018 ◽  
Vol 20 (3) ◽  
pp. 2009-2021 ◽  
Author(s):  
Xiaohui Wang ◽  
Xingzhao Tu ◽  
John Z. H. Zhang ◽  
Zhaoxi Sun
Keyword(s):  
Adaptive Sampling ◽  
Current Work ◽  
Variance Minimization ◽  
Acceptance Ratio ◽  
Bennett Acceptance Ratio ◽  
Sampling Regime

Following the previously proposed equilibrate-state sampling based adaptive sampling regime Optimum Bennett Acceptance Ratio (OBAR), we introduce its nonequilibrium extension, Optimum Crooks’ Equation (OCE) in the current work.

BAR-based optimum adaptive sampling regime for variance minimization in alchemical transformation

2017 ◽  
Vol 19 (23) ◽  
pp. 15005-15020 ◽  
Author(s):  
Zhao X. Sun ◽  
Xiao H. Wang ◽  
John Z. H. Zhang
Keyword(s):  
Free Energy ◽  
Importance Sampling ◽  
Adaptive Sampling ◽  
Variance Minimization ◽  
Sampling Regime ◽  
Energy Simulations ◽  
Alchemical Free Energy

The efficiency of alchemical free energy simulations with a staging strategy is improved by adaptively manipulating the significance of each ensemble followed by importance sampling.

scholarly journals Multiensemble Markov models of molecular thermodynamics and kinetics

2016 ◽  
Vol 113 (23) ◽  
pp. E3221-E3230 ◽  
Author(s):  
Hao Wu ◽  
Fabian Paul ◽  
Christoph Wehmeyer ◽  
Frank Noé
Keyword(s):  
Detailed Balance ◽  
Rare Event ◽  
Umbrella Sampling ◽  
High Dimensional ◽  
Acceptance Ratio ◽  
Markov State ◽  
Markov State Models ◽  
Bennett Acceptance Ratio ◽  
Special Cases ◽  
State Models

We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov state models—clustering of high-dimensional spaces and modeling of complex many-state systems—with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein–ligand binding model.

scholarly journals BAR-based Optimum Adaptive Steered MD for Configurational Sampling

2018 ◽  
Author(s):  
Xiaohui Wang ◽  
Xingzhao Tu ◽  
Boming Deng ◽  
John Z. H. Zhang ◽  
Zhaoxi Sun
Keyword(s):  
Free Energy ◽  
Adaptive Sampling ◽  
Sampling Method ◽  
Time Derivative ◽  
Selection Criterion ◽  
Equal Time ◽  
Acceptance Ratio ◽  
Configurational Space ◽  
State Discrimination ◽  
Bennett’S Acceptance Ratio

<p>Previously we proposed the equilibrium and nonequilibrium adaptive alchemical free energy simulation methods Optimum Bennett’s Acceptance Ratio (OBAR) and Optimum Crooks’ Equation (OCE). They are based on the statistically optimal bidirectional reweighting estimator named Bennett’s Acceptance Ratio (BAR) or Crooks’ Equation (CE). They perform initial sampling in the staging alchemical transformation and then determine the importance rank of different states via the time-derivative of the variance (TDV). The method is proven to give speedups compared with the equal time rule. In the current work, we extended the time derivative of variance guided adaptive sampling method to the configurational space, falling in the term of Steered MD (SMD). The SMD approach biasing physically meaningful collective variable (CV) such as one dihedral or one distance to pulling the system from one conformational state to another. By minimizing the variance of the free energy differences along the pathway in an optimized way, a new type of adaptive SMD (ASMD) is introduced. As exhibits in the alchemical case, this adaptive sampling method outperforms the traditional equal-time SMD in nonequilibrium stratification. Also, the method gives much more efficient calculation of potential of mean force than the selection criterion based ASMD scheme, which is proven to be more efficient than traditional SMD. The variance-linearly-dependent minus time derivative of overall variance proposed for OBAR and OCE criterion is extended to determine the importance rank of the nonequilibrium pulling in the configurational space. It is shown that the importance rank given by the standard deviation of the free energy difference is wrong, but by correcting it with the simulation time we obtain the true importance rank in nonequilibrium stratification. The OCE workflow is periodicity-of-CV dependent while ASMD is not. In the non-periodic CV case, the end-state discrimination in the SD rank is eliminated in the TDV rank, while in the periodic CV case the correction introduced in the TDV rank is not that significant. The performance is demonstrated in a dihedral flipping case and two distance pulling cases, accounting for periodic and non-periodic CVs, respectively. </p>

scholarly journals Building a Macro-mixing Dual-basin Go Model using the Multistate Bennett Acceptance Ratio

2020 ◽  
Vol 118 (3) ◽  
pp. 179a
Author(s):  
Ai Shinobu ◽  
Chigusa Kobayashi ◽  
Yasuhiro Matsunaga ◽  
Yuji Sugita
Keyword(s):  
Acceptance Ratio ◽  
Bennett Acceptance Ratio ◽  
Go Model
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