Neural transport method improves sampling of molecular systems
Neural Transport Nested Sampling
Machine Learning
Summary
Sampling the positions and energies of molecules accurately is a challenging problem in chemistry and physics. The authors developed a new method called Neural Transport Nested Sampling (NTNS) that uses neural networks combined with classical sampling techniques to better predict molecular behavior. Their approach works well even for complex systems with many particles, producing more accurate results faster than previous neural network methods. NTNS also estimates important physical properties like temperature-dependent phase changes from a single run.
What this means in practice
- •For computational chemists: Model molecular systems more efficiently with improved accuracy and estimate temperature-dependent properties from a single simulation.
- •For materials simulation teams: Simulate particle interactions in high-dimensional systems with lower computational cost and higher fidelity than previous neural samplers.
Authors
David Yallup, Will Handley
Abstract
Sampling from Boltzmann distributions of molecular systems is an inference problem that has seen significant recent developments fuelled by advances in neural density estimation. We develop a novel sampling algorithm, Neural Transport Nested Sampling (NTNS), which combines the classical strengths of nested sampling with modern neural flow-based methods. NTNS uses a flow matching velocity as the drift in a Metropolis--Hastings corrected Langevin kernel inside a nested sampling outer loop, requiring only evaluations of the target energy function and providing scalable estimation of the full partition function of high-dimensional particle systems. We benchmark NTNS on challenging molecular sampling benchmarks, scaling up to Lennard--Jones clusters of 55 interacting particles, where it reduces both interatomic distance and energy Wasserstein errors to reference MCMC by over an order of magnitude relative to the strongest neural baselines at lower wall-clock cost. To our knowledge, NTNS is also the first neural sampler to return a calibrated, temperature resolved partition function estimate at this scale, recovering the phase structure across temperature from a single run.