arXiv Machine Learning
Langevin Flow Maps: Efficient Molecular Dynamics and Transition Path Sampling
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arXiv Machine Learning
Jul 1
Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
arXiv:2601. 22123v4 Announce Type: replace Abstract: Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration.
arXiv Statistics ML
Oct 2
Langevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedback
The paper introduces Langevin-Informed Transfer Learning (LITL), a method that recovers target Langevin dynamics from biased source samples using only black-box feedback. LITL learns the leading spectral structure of the target infinitesimal generator and the projected drift via Dirichlet representation learning, enabling kinetic reconstruction and slow-manifold gradient estimation. The authors provide finite-sample guarantees for eigenvalue, eigenfunction, and drift estimation, and demonstrate LITL’s ability to recover physical transition timescales, build kinetic structure from static generative samples, reconstruct spherical symmetries, and steer latent representations in trained neural networks.
arXiv Machine Learning
Sep 25
Neural Transport Nested Sampling
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.
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.
arXiv Machine Learning
Jul 9
Gradient-free Riemannian Langevin Sampler
arXiv:2607. 07519v1 Announce Type: new Abstract: We address the problem of efficiently sampling multimodal probability distributions, where standard Markov Chain Monte Carlo methods often suffer from poor mixing and mode trapping.
Hugging Face Trending Papers
Jul 8
Gradient-free Riemannian Langevin Sampler
We address the problem of efficiently sampling multimodal probability distributions, where standard Markov Chain Monte Carlo methods often suffer from poor mixing and mode trapping. To mitigate these issues, we propose Gradient-free Riemannian Langevin Sampler (GRiLS), a novel proposal that improves exploration without requiring gradient evaluations of the target density.