138 citations · 213 across the 8 of their papers we have counts for
5 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…
NATPS: Nonadiabatic Transition Path Sampling Using Time-Reversible MASH Dynamics
Xiran Yang, Madlen Maria Reiner, Brigitta Bachmair +3
Rare nonadiabatic events play a central role in photochemistry but remain difficult to simulate because excited-state dynamics is computationally demanding and often stochastic. He…
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…