arXiv Machine Learning September 18, 2026 By Maximilian Negedly, Sebastian Falkner, Alessandro Coretti, Christoph Dellago

Correlation-Free Transition Path Sampling through Shooting Point Generation Guided by Committor Learning

Read the original on arXiv Machine Learning →

The paper introduces GenAIMMD, an iterative algorithm that learns the committor function and trains a conditioned Boltzmann Generator to generate uncorrelated transition paths without prior knowledge of the reaction coordinate. This method combines transition path sampling with committor learning, enabling fully parallelizable sampling. Benchmarks on a toy model and a polymer system show a substantial performance improvement over standard TPS.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

2d ago

Machine learning kinetics from molecular dynamics data

arXiv:2609.17736v1 Announce Type: cross Abstract: Most molecular transitions occur on timescales far beyond direct molecular dynamics simulations. The committor, the probability that a configuration...

By Jonathan Weare, Aaron R. Dinner

reinforcement-learning

arXiv Machine Learning

Jul 20

Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling

arXiv:2607. 15682v1 Announce Type: new Abstract: Sampling from an unnormalized Boltzmann density requires proposals that move probability mass globally while retaining enough path-probability information for statistical correction.

By Moxian Qian

arXiv Machine Learning

Jun 4

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials

arXiv:2606. 04100v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data.

By Joanna Zou, Fraser Birks, Dallas Foster, Youssef Marzouk

fine-tuning

arXiv AI

Jun 26

Autoregressive Boltzmann Generators

arXiv:2606. 27361v1 Announce Type: cross Abstract: Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics.

By Danyal Rehman, Charlie B. Tan, Yoshua Bengio, Avishek Joey Bose, Alexander Tong

llms benchmarks

arXiv Machine Learning

Aug 17

Stochastic Control Policies for Robust Molecular Transition Path Sampling

arXiv:2608. 13800v1 Announce Type: new Abstract: Transition path sampling (TPS) aims to efficiently generate rare molecular transition trajectories between metastable states and is essential for understanding biomolecular mechanisms.

By Jingqian Liu, Yu-Hsiang Wang, Yanru Qu, Ge Liu

benchmarks

Hugging Face Trending Papers

Jun 4

Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events

Path sampling methods generate ensembles of reactive trajectories connecting metastable states, but extracting mechanistic insight from these data remains nontrivial. We introduce Flux Matching, a framework that learns two complementary objects directly from reactive trajectory data: a current velocity $u(z)$, whose streamlines trace the dominant reaction pathways, and a scalar potential $h(z)$, obtained from a weighted Helmholtz-Hodge decomposition of the reactive current, that serves as a data-driven reaction coordinate.

diffusion

One email a morning, machine-written

Related stories

Machine learning kinetics from molecular dynamics data

Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials

Autoregressive Boltzmann Generators

Stochastic Control Policies for Robust Molecular Transition Path Sampling

Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events