2 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
Correlation-Free Transition Path Sampling through Shooting Point Generation Guided by Committor Learning
Maximilian Negedly, Sebastian Falkner, Alessandro Coretti +1
Studying the dynamical behavior of a system often depends on characterizing how it transitions between long-lived states. Because such transitions are rare, observing them usually…
An Always-Accepting Algorithm for Transition Path Sampling
Magdalena Häupl, Sebastian Falkner, Peter G. Bolhuis +2
We present a one-way shooting algorithm for transition path sampling that accepts every proposed trajectory, yet samples the correct transition path ensemble for systems with overd…
Revisiting Shooting Point Monte Carlo Methods for Transition Path Sampling
Sebastian Falkner, Alessandro Coretti, Baron Peters +2
Rare event sampling algorithms are essential for understanding processes that occur infrequently on the molecular scale, yet they are important for the long-time dynamics of comple…
Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems
Alessandro Coretti, Sebastian Falkner, Jan Weinreich +2
The paper by Noé et al. [F. Noé, S. Olsson, J. Köhler and H. Wu, Science, 365:6457 (2019)] introduced the concept of Boltzmann Generators (BGs), a deep generative model that can pr…