8 papers
Discovering Reaction Mechanisms with Transition Path Sampling-Based Active Learning of Machine-Learned Potentials
Ashique Lal, Rik S. Breebaart, Peter G. Bolhuis +1
Machine-learned interatomic potentials (MLPs) provide near density functional theory (DFT) accuracy at reduced computational cost, but their reliability depends on representative t…
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…
Combining multiple interface set path ensembles with MBAR reweighting
Rik S. Breebaart, Peter G. Bolhuis
We introduce a method to compute the reweighted path ensemble by combining transition interface sampling simulations conditioned on different collective variables. The approach is…
Understanding Reaction Mechanisms from Start to Finish
Rik S. Breebaart, Gianmarco Lazzeri, Roberto Covino +1
Understanding mechanisms of rare but important events in complex molecular systems, such as protein folding or ligand (un)binding, requires accurately mapping transition paths from…
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…
Kinetic phase diagram for two-step nucleation in colloid-polymer mixtures
Willem Gispen, Peter G. Bolhuis, Marjolein Dijkstra
Two-step crystallization via a metastable intermediate phase is often regarded as a non-classical process that lies beyond the framework of classical nucleation theory (CNT). In th…