mlsanityData to 7 Oct 2026

Generative methods for sampling transition paths in molecular dynamics

arXiv 2205.02818 ↗First posted 5 May 2022stat.ML cs.LGcited by 6

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Abstract (arXiv)

Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastability. Simulating transition paths linking one metastable state to another one is difficult by direct numerical methods. In view of the promises of machine learning techniques, we explore in this work two approaches to more efficiently generate transition paths: sampling methods based on generative models such as variational autoencoders, and importance sampling methods based on reinforcement learning.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 53 references that are in the map. First posted on arXiv 5 May 2022.

Builds on

17 of 53 references.

References · 17

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5 more
  1. 2021Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formationcited by 6
  2. 2019Learning to grow: control of material self-assembly using evolutionary reinforcement learningcited by 5
  3. 2020Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalizationcited by 5
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 0; 2026: 1.

6 papers.

  1. 2024Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Samplingcited by 25
  2. 2024Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplerscited by 10
  3. 2023Diffusion Methods for Generating Transition Pathscited by 8
  4. 2023Probing reaction channels via reinforcement learningcited by 1
  5. 2024Computing Transition Pathways for the Study of Rare Events Using Deep Reinforcement Learningcited by 0
  6. 2026Machine learning kinetics from molecular dynamics datacited by 0