arXiv:2605. 31498v2 Announce Type: replace Abstract: A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules.
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.
Neural Transport Nested Sampling (NTNS) is a new sampling algorithm that merges nested sampling with neural flow-based methods to sample from Boltzmann distributions of molecular systems. It employs a flow-matching velocity as the drift in a Metropolis–Hastings corrected Langevin kernel within a nested sampling loop, requiring only target energy evaluations and enabling scalable estimation of the full partition function for high-dimensional particle systems. Benchmarks on Lennard–Jones clusters up to 55 particles show NTNS reduces interatomic distance and energy Wasserstein errors by more than an order of magnitude compared to leading neural baselines, while also providing a calibrated, temperature-resolved partition function estimate that captures phase structure from a single run.
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.
arXiv:2606. 27361v1 Announce Type: cross Abstract: Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics.
arXiv:2606. 29110v1 Announce Type: new Abstract: Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations.
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:2608. 07648v1 Announce Type: cross Abstract: Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecular simulation.
arXiv:2601. 21026v2 Announce Type: replace-cross Abstract: Sampling configurations at thermodynamic equilibrium is a central challenge in statistical physics.
arXiv:2602. 24007v3 Announce Type: replace-cross Abstract: Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles that match experimental data.
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.
arXiv:2606. 30228v1 Announce Type: new Abstract: Modern engineering workflows increasingly rely on massive parallel simulation, driving the need for scalable, large-batch Bayesian Optimization (BO).
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.