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Research PaperResearchia:202606.02024

Speculative Sampling For Faster Molecular Dynamics

Arthur Kosmala

Abstract

Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system throughput with concurrent compute. To address this, we introduce Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampler for accelerating MD without adding relative error. Inspired by speculative methods in language and diffusion modeling, LSD uses a draft model to propose fast simula...

Submitted: June 2, 2026Subjects: Machine Learning; Data Science

Description / Details

Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system throughput with concurrent compute. To address this, we introduce Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampler for accelerating MD without adding relative error. Inspired by speculative methods in language and diffusion modeling, LSD uses a draft model to propose fast simulation steps and verifies them in parallel with a slower target model, applying a transport map from the draft to the target distribution. We extend speculative sampling to second-order Langevin dynamics, derive the achievable speedup as a function of physical parameters, show that LSD generalizes across different systems and draft-target combinations with a 3-9x speedup, and confirm theoretically and empirically that LSD samples trajectories from its target model distribution.


Source: arXiv:2606.02455v1 - http://arxiv.org/abs/2606.02455v1 PDF: https://arxiv.org/pdf/2606.02455v1 Original Link: http://arxiv.org/abs/2606.02455v1

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Date:
Jun 2, 2026
Topic:
Data Science
Area:
Machine Learning
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