TY - JOUR
T1 - Inference of non-exponential kinetics through stochastic resetting
AU - Blumer, Ofir
AU - Reuveni, Shlomi
AU - Hirshberg, Barak
N1 - Publisher Copyright:
© 2024 Author(s).
PY - 2024/12/14
Y1 - 2024/12/14
N2 - 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 ∼ 1 μ s . 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 peptide in an explicit solvent, successfully estimating the unbiased mean first-passage time while accelerating the sampling by more than an order of magnitude.
AB - 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 ∼ 1 μ s . 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 peptide in an explicit solvent, successfully estimating the unbiased mean first-passage time while accelerating the sampling by more than an order of magnitude.
UR - http://www.scopus.com/inward/record.url?scp=85212203128&partnerID=8YFLogxK
U2 - 10.1063/5.0243783
DO - 10.1063/5.0243783
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C2 - 39651949
AN - SCOPUS:85212203128
SN - 0021-9606
VL - 161
JO - Journal of Chemical Physics
JF - Journal of Chemical Physics
IS - 22
M1 - 224104
ER -