Inference of non-exponential kinetics through stochastic resetting
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909347511009280 |
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| author | Blumer, Ofir Reuveni, Shlomi Hirshberg, Barak |
| author_facet | Blumer, Ofir Reuveni, Shlomi Hirshberg, Barak |
| contents | We present an inference scheme of long timescale, non-exponential kinetics from Molecular Dynamics simulations accelerated by stochastic resetting. Standard simulations provide valuable insight into chemical processes but are limited to timescales shorter than $\sim 1 μs$. Slower processes require the use of enhanced sampling methods to expedite them, and inference schemes to obtain the unbiased kinetics. However, most kinetics inference schemes assume an underlying exponential first-passage time distribution and are inappropriate for other distributions, e.g., with a power-law decay. We propose an inference scheme that is designed for such cases, based on simulations enhanced by stochastic resetting. We show that resetting promotes enhanced sampling of the first-passage time distribution at short timescales, but often also provides sufficient information to estimate the long-time asymptotics, which allows the kinetics inference. We apply our method to a model system and a short peptide in an explicit solvent, successfully estimating the unbiased mean first-passage time while accelerating the sampling by more than an order of magnitude. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_09805 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inference of non-exponential kinetics through stochastic resetting Blumer, Ofir Reuveni, Shlomi Hirshberg, Barak Chemical Physics Statistical Mechanics Biological Physics We present an inference scheme of long timescale, non-exponential kinetics from Molecular Dynamics simulations accelerated by stochastic resetting. Standard simulations provide valuable insight into chemical processes but are limited to timescales shorter than $\sim 1 μs$. Slower processes require the use of enhanced sampling methods to expedite them, and inference schemes to obtain the unbiased kinetics. However, most kinetics inference schemes assume an underlying exponential first-passage time distribution and are inappropriate for other distributions, e.g., with a power-law decay. We propose an inference scheme that is designed for such cases, based on simulations enhanced by stochastic resetting. We show that resetting promotes enhanced sampling of the first-passage time distribution at short timescales, but often also provides sufficient information to estimate the long-time asymptotics, which allows the kinetics inference. We apply our method to a model system and a short peptide in an explicit solvent, successfully estimating the unbiased mean first-passage time while accelerating the sampling by more than an order of magnitude. |
| title | Inference of non-exponential kinetics through stochastic resetting |
| topic | Chemical Physics Statistical Mechanics Biological Physics |
| url | https://arxiv.org/abs/2410.09805 |