Transition Path Sampling with Boltzmann Generator-based MCMC Moves
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911890814271488 |
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| author | Plainer, Michael Stärk, Hannes Bunne, Charlotte Günnemann, Stephan |
| author_facet | Plainer, Michael Stärk, Hannes Bunne, Charlotte Günnemann, Stephan |
| contents | Sampling all possible transition paths between two 3D states of a molecular system has various applications ranging from catalyst design to drug discovery. Current approaches to sample transition paths use Markov chain Monte Carlo and rely on time-intensive molecular dynamics simulations to find new paths. Our approach operates in the latent space of a normalizing flow that maps from the molecule's Boltzmann distribution to a Gaussian, where we propose new paths without requiring molecular simulations. Using alanine dipeptide, we explore Metropolis-Hastings acceptance criteria in the latent space for exact sampling and investigate different latent proposal mechanisms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05340 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Transition Path Sampling with Boltzmann Generator-based MCMC Moves Plainer, Michael Stärk, Hannes Bunne, Charlotte Günnemann, Stephan Quantitative Methods Machine Learning Sampling all possible transition paths between two 3D states of a molecular system has various applications ranging from catalyst design to drug discovery. Current approaches to sample transition paths use Markov chain Monte Carlo and rely on time-intensive molecular dynamics simulations to find new paths. Our approach operates in the latent space of a normalizing flow that maps from the molecule's Boltzmann distribution to a Gaussian, where we propose new paths without requiring molecular simulations. Using alanine dipeptide, we explore Metropolis-Hastings acceptance criteria in the latent space for exact sampling and investigate different latent proposal mechanisms. |
| title | Transition Path Sampling with Boltzmann Generator-based MCMC Moves |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2312.05340 |