2021
DOI: 10.1101/2021.12.05.471280
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WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications

Abstract: The weighted ensemble (WE) family of methods is one of several statistical-mechanics based path sampling strategies that can provide estimates of key observables (rate constants, pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on… Show more

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“…WE simulations were run in the following manner using the Python API of the WESTPA 2.0 software package. 56 To maintain non-equilibrium steady state conditions, trajectories that reached a target state of compartment A or D (i.e., z = 3.0 nm, see Figure 1) were "recycled", starting a new trajectory from the initial state (compartment). A one-dimensional progress coordinate was divided into bins using two different schemes: a manual, fixed binning scheme and the minimal adaptive binning (MAB) scheme 57 (see below).…”
Section: Weighted Ensemble Simulationsmentioning
confidence: 99%
“…WE simulations were run in the following manner using the Python API of the WESTPA 2.0 software package. 56 To maintain non-equilibrium steady state conditions, trajectories that reached a target state of compartment A or D (i.e., z = 3.0 nm, see Figure 1) were "recycled", starting a new trajectory from the initial state (compartment). A one-dimensional progress coordinate was divided into bins using two different schemes: a manual, fixed binning scheme and the minimal adaptive binning (MAB) scheme 57 (see below).…”
Section: Weighted Ensemble Simulationsmentioning
confidence: 99%