Infrequent Metadynamics is a popular method to obtain the rates of long time-scale processes from accelerated molecular dynamics simulations. We propose to improve kinetics inference in this method by focusing on short time scales. We demonstrate the new inference scheme for a model system and two molecular systems. We show an improved trade-off between speedup and accuracy at no additional computational cost, especially when employing suboptimal collective variables.

Read our preface for this special issue of the Journal of Physics A. Stochastic Resetting — Theory and Applications. In Celebration of the 10th Anniversary of 'Diffusion with Stochastic Resetting'.

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The Montroll-Weiss continuous time random walk has found numerous applications due its ease of use and ability to describe both regular and anomalous diffusion. Yet, despite its broad applicability and generality, the model cannot account for effects coming from random diffusivity fluctuations, which have been observed in the motion of asset prices and molecules. To bridge this gap, we introduce a doubly stochastic version of the model in which waiting times between jumps are replaced with a fluctuating jump rate. 

We combine stochastic resetting with Metadynamics and show that this can accelerate molecular dynamics simulations beyond either method separately. We also show that applying stochastic resetting can be an alternative to the challenging task of finding optimal collective variables for Metadynamics, at almost no additional computational cost. Finally, we propose a method to extract unbiased mean first-passage times from Metadynamics simulations with resetting, resulting in an improved tradeoff between speedup and accuracy. 

We present a robust and general approach to bacterial identification based on their unique enzymatic activity profiles. This method delivers results within 90 minutes, utilizing an array of highly sensitive and enzyme-selective chemiluminescent probes.  The method opens new avenues for characterizing and identifying pathogens in research, clinical, and industrial applications.

How much time does it take for a sticky particle to diffuse out of a given compartment? We develop an analytical framework to solve this open problem, which has applications in biophysics and nanoscience. This is used to elucidate the effect of stickiness on the escape time from a slab domain, revealing that adsorption and desorption rates (which are largely unknown) can be inferred from the mean and variance of the escape time. An efficient scheme for simulating “sticky escape” from arbitrary domains is also presented.