We introduce a rare-event sampling scheme, named Markovian Weighted Ensemble
Milestoning (M-WEM), which inlays a weighted ensemble framework within a Markovian …

Elucidating physical mechanisms with statistical confidence from molecular dynamics
simulations can be challenging owing to the many degrees of freedom that contribute to …

Deep neural networks, when optimized with sufficient data, provide accurate representations
of high-dimensional functions; in contrast, function approximation techniques that have …

Transition path theory computes statistics from ensembles of reactive trajectories. A common
strategy for sampling reactive trajectories is to control the branching and pruning of …

The protein hormone insulin exists in various oligomeric forms, and a key step in binding its
cellular receptor is dissociation of the dimer. This dissociation process and its corresponding …

Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic
dynamical systems. When the event is rare in comparison with the timescales of simulation …

The use of neural network parametrizations to represent the ground state in variational
Monte Carlo (VMC) calculations has generated intense interest in recent years. However, as …

We introduce an ensemble Markov chain Monte Carlo approach to sampling from a
probability density with known likelihood. This method upgrades an underlying Markov …

We present a general mathematical framework for trajectory stratification for simulating rare
events. Trajectory stratification involves decomposing trajectories of the underlying process …

Importance sampling is a popular variance reduction method for Monte Carlo estimation,
where an evident question is how to design good proposal distributions. While in most cases …