[2510.18460] Learning Boltzmann Generators via Constrained Mass Transport

Computer Science > Machine Learning arXiv:2510.18460 (cs) [Submitted on 21 Oct 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:Learning Boltzmann Generators via Constrained Mass Transport Authors:Christopher von Klitzing, Denis Blessing, Henrik Schopmans, Pascal Friederich, Gerhard Neumann View a PDF of the paper titled Learning Boltzmann Generators via Constrained Mass Transport, by Christopher von Klitzing and 4 other authors View PDF HTML (experimental) Abstract:Efficient sampling from high-dimensional and multimodal unnormalized probability distributions is a central challenge in many areas of science and machine learning. We focus on Boltzmann generators (BGs) that aim to sample the Boltzmann distribution of physical systems, such as molecules, at a given temperature. Classical variational approaches that minimize the reverse Kullback-Leibler divergence are prone to mode collapse, while annealing-based methods, commonly using geometric schedules, can suffer from mass teleportation and rely heavily on schedule tuning. We introduce Constrained Mass Transport (CMT), a variational framework that generates intermediate distributions under constraints on both the KL divergence and the entropy decay between successive steps. These constraints enhance distributional overlap, mitigate mass teleportation, and counteract premature convergence. Across standard BG benchmarks and the here introduced ELIL tetrapeptide, the largest system studied to date without access t...