206 citations · 771 across the 22 of their papers we have counts for
3 papers · 1 filter
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Girsanov Reweighting for Uncertainty Propagation in Rare-Event Kinetics
Leonard Moracchini, Thomas Pigeon, Morgane Menz +3
Machine-learning interatomic potentials (MLIPs) have become a powerful tool for rare event sampling in molecular dynamics, offering near ab initio accuracy at a fraction of the com…
Estimating time-correlation functions by sampling and unbiasing dynamically activated events
Manuel Athènes, Mihai-Cosmin Marinica, Thomas Jourdan
Transition path sampling is a rare-event method that estimates state-to-state timecorrelation functions in many-body systems from samples of short trajectories. In this framework,…