An exact multiple-time-step variational formulation for the committor and the transition rate
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915658515611648 |
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| author | Lorpaiboon, Chatipat Weare, Jonathan Dinner, Aaron R. |
| author_facet | Lorpaiboon, Chatipat Weare, Jonathan Dinner, Aaron R. |
| contents | For a transition between two stable states, the committor is the probability that the dynamics leads to one stable state before the other. It can be estimated from trajectory data by minimizing an expression for the transition rate that depends on a lag time. We show that an existing such expression is minimized by the exact committor only when the lag time is a single time step, resulting in a biased estimate in practical applications. We introduce an alternative expression that is minimized by the exact committor at any lag time. The key idea is that, when trajectories enter the stable states, the times that they enter (stopping times) must be used for estimating the committor and transition rate instead of the lag time. Numerical tests on benchmark systems demonstrate that our committor and transition rate estimates are much less sensitive to the choice of lag time. We show how further accuracy for the transition rate can be achieved by combining results from two lag times. We also relate the transition rate expression to a variational approach for kinetic statistics based on the mean-squared residual and discuss further numerical considerations with the aid of a decomposition of the error into dynamic modes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03539 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An exact multiple-time-step variational formulation for the committor and the transition rate Lorpaiboon, Chatipat Weare, Jonathan Dinner, Aaron R. Statistical Mechanics Machine Learning Computational Physics Data Analysis, Statistics and Probability For a transition between two stable states, the committor is the probability that the dynamics leads to one stable state before the other. It can be estimated from trajectory data by minimizing an expression for the transition rate that depends on a lag time. We show that an existing such expression is minimized by the exact committor only when the lag time is a single time step, resulting in a biased estimate in practical applications. We introduce an alternative expression that is minimized by the exact committor at any lag time. The key idea is that, when trajectories enter the stable states, the times that they enter (stopping times) must be used for estimating the committor and transition rate instead of the lag time. Numerical tests on benchmark systems demonstrate that our committor and transition rate estimates are much less sensitive to the choice of lag time. We show how further accuracy for the transition rate can be achieved by combining results from two lag times. We also relate the transition rate expression to a variational approach for kinetic statistics based on the mean-squared residual and discuss further numerical considerations with the aid of a decomposition of the error into dynamic modes. |
| title | An exact multiple-time-step variational formulation for the committor and the transition rate |
| topic | Statistical Mechanics Machine Learning Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2509.03539 |