The paper introduces NS‑Flows, a flow‑based nested sampling method that replaces Markov‑chain updates with a conditional normalizing flow trained on live sets. By applying this technique to a Lennard‑Jones particle system, the authors achieve over two orders of magnitude fewer energy evaluations and a roughly one‑third reduction in wall‑clock time compared to traditional nested sampling. The study also shows that the flow’s generation efficiency varies non‑monotonically along the annealing trajectory, providing a diagnostic of the system’s internal mode complexity and identifying liquid‑like ensembles as the most challenging for current flow architectures.
By Alessandro Coretti, Nico Unglert, Sebastian Falkner, Georg K. H. Madsen, Christoph Dellago
arXiv:2607. 03513v1 Announce Type: cross Abstract: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD).
By Emmanuel Bengio, Sanjeev Raja, Yui Tik Pang, Kerstin Klaeser, Cristian Gabellini, Nikhil Shenoy, Francesco Di Giovanni, Prudencio Tossou
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
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
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann Generators (BGs), which allow rapid generation of uncorrelated equilibrium samples by combining a generative model with exact likelihoods and an importance sampling correction.
arXiv:2602. 16634v2 Announce Type: replace-cross Abstract: The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation.
By Yu Xie, Ludwig Winkler, Lixin Sun, Sarah Lewis, Adam E. Foster, Jos\'e Jim\'enez Luna, Tim Hempel, Michael Gastegger, Yaoyi Chen, Iryna Zaporozhets, Cecilia Clementi, Christopher M. Bishop, Frank No\'e
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:2607. 27077v1 Announce Type: new Abstract: Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability.
By Nicolas B\'ereux, Aur\'elien Decelle, Cyril Furtlehner, Beatriz Seoane
arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.
By Riccardo De Santi, Bruce Lee, Cristian Perez Jensen, Kimon Protopapas, Sophia Tang, Cheng-Hao Liu, Pranam Chatterjee, Yisong Yue, Andreas Krause
Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability. We introduce a training algorithm based on Parallel Trajectory Tempering (PTT), which exploits the continuity of the optimization path to maintain equilibrium sampling throughout 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.
By Maximilian Negedly, Sebastian Falkner, Alessandro Coretti, Christoph Dellago
The paper introduces Langevin simulated bifurcation (LSB), a fast, parallel Boltzmann sampler that matches the accuracy of sequential MCMC methods. It also proposes conditional expectation matching (CEM), an efficient technique for estimating the effective temperature of samples from energy‑based models with conditional independence. Building on these, the authors develop sampler adaptive learning (SAL), which adjusts the model temperature to align with the distribution produced by LSB, enabling efficient training of semi‑restricted Boltzmann machines (SRBMs) and outperforming conventional methods on synthetic spin‑glass datasets.
By Kentaro Kubo, Hayato Goto