75 citations · 128 across the 10 of their papers we have counts for
10 papers
Reducing Weighted Ensemble Variance With Optimal Trajectory Management
Won Hee Ryu, John D. Russo, Mats S. Johnson +7
Weighted ensemble (WE) is an enhanced path-sampling method that is conceptually simple, widely applicable, and statistically exact. In a WE simulation, an ensemble of trajectories…
Featurizing Koopman Mode Decomposition For Robust Forecasting
David Aristoff, Jeremy Copperman, Nathan Mankovich +1
This article introduces an advanced Koopman mode decomposition (KMD) technique -- coined Featurized Koopman Mode Decomposition (FKMD) -- that uses delay embedding and a learned Mah…
Weighted ensemble: Recent mathematical developments
D. Aristoff, J. Copperman, G. Simpson +2
The weighted ensemble (WE) method, an enhanced sampling approach based on periodically replicating and pruning trajectories in a set of parallel simulations, has grown increasingly…
Unbiased estimation of equilibrium, rates, and committors from Markov state model analysis
John D. Russo, Jeremy Copperman, David Aristoff +2
Markov state models (MSMs) have been broadly adopted for analyzing molecular dynamics trajectories, but the approximate nature of the models that results from coarse-graining into…
Iterative trajectory reweighting for estimation of equilibrium and non-equilibrium observables
John D. Russo, Jeremy Copperman, Daniel M. Zuckerman
We present two algorithms by which a set of short, unbiased trajectories can be iteratively reweighted to obtain various observables. The first algorithm estimates the stationary (…
Accelerated estimation of long-timescale kinetics by combining weighted ensemble simulation with Markov model "microstates" using non-Markovian theory
Jeremy Copperman, Daniel Zuckerman
The weighted ensemble (WE) simulation strategy provides unbiased sampling of non-equilibrium processes, such as molecular folding or binding, but the extraction of rate constants r…