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

arXiv:2609.20461 · physics.comp-ph, cond-mat.stat-mech, cs.LG · Submitted 2026-09-17 · Read on arXiv

physics.comp-ph, cond-mat.stat-mech, cs.LG

Submitted: 2026-09-17

Updated: 2026-09-17

Code: https://github.com/CompPhysVienna/paper_genaimmd

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transition Path Sampling — 14× in this paper
• Normalizing Flow — 9× in this paper · explained in 3 episodes
• Normalizing Flows — 9× in this paper · explained in 4 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Self-consistency — 7× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Active Learning — 5× in this paper · explained in 6 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Effective Sample Size — 3× in this paper
• Latent Space — 3× in this paper · explained in 7 episodes
• Artificial Intelligence for Molecular Mechanism Discovery — 2× in this paper
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Relative Effective Sample Size — 2× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• Langevin Dynamics — 1× in this paper · explained in 2 episodes
• Phase Transitions — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Enhanced sampling methods for molecular dynamics simulations
• Differentiable free energy surface: a variational approach to directly observing rare events using generative deep-learning models
• Density estimation using Real NVP
• Pareto Smoothed Importance Sampling
• Neural Spline Flows
• Scalable Equilibrium Sampling with Sequential Boltzmann Generators

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