Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
Microsoft Research AI for Science（微软研究院人工智能科学部）
Yu Xie, Ludwig Winkler, Lixin Sun, Sarah Lewis, Adam E. Foster, José Jiménez Luna, Tim Hempel, Michael Gastegger, Yaoyi Chen, Iryna Zaporozhets, Cecilia Clement… 展开作者
Yu Xie, Ludwig Winkler, Lixin Sun, Sarah Lewis, Adam E. Foster, José Jiménez Luna, Tim Hempel, Michael Gastegger, Yaoyi Chen, Iryna Zaporozhets, Cecilia Clementi, Christopher M. Bishop, Frank Noé
The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation. Recently, diffusion models such as BioEmu have emerged as powerful equilibrium samplers that generate independent samples from complex molecular distributions, eliminating the cost of sampling rare transition events. However, a sampling problem remains when computing observables that rely on states which are rare in equilibrium, for example folding free energies. Here, we introduce enhanced diffusion sampling, enabling efficient exploration of rare-event regions while preserving unbiased thermodynamic estimators. The key idea is to perform quantitatively accurate steering protocols to generate biased ensembles and subsequently recover equilibrium statistics via exact reweighting. We instantiate our framework in three algorithms: UmbrellaDiff (umbrella sampling with diffusion models), MetaDiff (a batchwise analogue for metadynamics), and $Δ$G-Diff (free-energy differences via tilted ensembles). Across toy systems, protein folding landscapes and folding free energies, our methods achieve fast, accurate, and scalable estimation of equilibrium properties within GPU-minutes to hours per system-closing the rare-event sampling gap that remained after the advent of diffusion-model equilibrium samplers.
Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events
CMU-Pitt Program in Computational Biology（CMU-匹兹堡计算生物学项目）；Dept. of Computational & Systems Biology University of Pittsburgh（计算与系统生物学系匹兹堡大学）；Machine Learning Department Dept. of Mathematical Sciences Carnegie Mellon University（机器学习系数学科学系卡内基梅隆大学）；Courant Institute, New York University（纽约大学Courant研究所）；Machine Learning Lab Capital Fund Management（机器学习实验室资本基金管理）
Quantum-Enhanced Generative Models for Rare Event Prediction
Department of Software Engineering(ÉTS), Université du Québec（魁北克大学软件工程系）；Department Of Computational Sciences,The University of Faisalabad（费萨利班大学计算科学系）；Department of Computer Science, SZABIST University（SZABIST大学计算机科学系）
Deep neural networks, when optimized with sufficient data, provide accurate representations of high-dimensional functions; in contrast, function approximation techniques that have predominated in scientific computing do not scale well with dimensionality. As a result, many high-dimensional sampling and approximation problems once thought intractable are being revisited through the lens of machine learning. While the promise of unparalleled accuracy may suggest a renaissance for applications that require parameterizing representations of complex systems, in many applications gathering sufficien…
University of Connecticut（康涅狄格大学）；ByteDance Inc.（字节跳动）
We investigate the issue of parameter estimation with nonuniform negative sampling for imbalanced data. We first prove that, with imbalanced data, the available information about unknown parameters is only tied to the relatively small number of positive instances, which justifies the usage of negative sampling. However, if the negative instances are subsampled to the same level of the positive cases, there is information loss. To maintain more information, we derive the asymptotic distribution of a general inverse probability weighted (IPW) estimator and obtain the optimal sampling probability…
Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events. These techniques often leverage the knowledge and analysis on underlying system structures to endow desirable efficiency guarantees. However, black-box problems, especially those arising from recent safety-critical applications of AI-driven physical systems, can fundamentally undermine their efficiency guarantees and lead to dangerous under-estimation without diagnostically detected. We propose a framework called Deep Probabilistic Accele…
AI-boosted rare event sampling to characterize extreme weather
LMD/IPSL, CNRS, ENS, Université PSL, École Polytechnique, Institut Polytechnique de Paris, Sorbonne Université（巴黎地球物理研究所/法国国家科学研究中心/巴黎高等师范学院/巴黎文理研究大学/巴黎综合理工学院/巴黎理工学院/索邦大学）；Réseau de Transport d’Électricité (RTE)（法国电力电网）；Department of the Geophysical Sciences, University of Chicago（芝加哥大学地球科学系）；Courant Institute, New York University（纽约大学库朗数学科学研究所）
