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Fast and accurate sampling method is in high demand, in order to bridge the large gaps between molecular dynamic simulations and experimental observations. Recently, integrated tempering enhanced sampling method (ITS) has been proposed and…

数值分析 · 数学 2018-06-22 Zhiyi You , Liying Li , Jianfeng Lu , Hao Ge

The simulation of rare events is one of the key problems in atomistic simulations. Towards its solution a plethora of methods have been proposed. Here we combine two such methods metadynamics and inte-grated tempering sampling. In…

化学物理 · 物理学 2018-10-29 Yi Isaac Yang , Haiyang Niu , Michele Parrinello

Umbrella sampling is an efficient method for the calculation of free energy changes of a system along well-defined reaction coordinates. However, when multiple parallel channels along the reaction coordinate or hidden barriers in directions…

统计方法学 · 统计学 2015-06-16 Mingjun Yang , Lijiang Yang , Yiqin Gao , Hao Hu

We investigate the theoretical foundations of the simulated tempering method and use our findings to design efficient algorithms. Employing a large deviation argument first used for replica exchange molecular dynamics [Plattner et al., J.…

化学物理 · 物理学 2019-02-08 Anton Martinsson , Jianfeng Lu , Benedict Leimkuhler , Eric Vanden-Eijnden

Simulated tempering (ST) is an established Markov chain Monte Carlo (MCMC) method for sampling from a multimodal density $\pi(\theta)$. Typically, ST involves introducing an auxiliary variable $k$ taking values in a finite subset of $[0,1]$…

统计计算 · 统计学 2008-11-03 Robert B. Gramacy , Richard J. Samworth , Ruth King

We present here two novel algorithms for simulated tempering simulations, which break detailed balance condition (DBC) but satisfy the skewed detailed balance to ensure invariance of the target distribution. The irreversible methods we…

统计力学 · 物理学 2021-02-03 Fahim Faizi , Pedro J. Buigues , George Deligiannidis , Edina Rosta

Thermodynamic properties can be in principle derived from the partition function, which, in many-atom systems, is hard to evaluate as it involves a sum on the accessible microscopic states. Recently, the partition function has been computed…

A variant of the parallel tempering method is proposed in terms of a stochastic switching process for the coupled dynamics of replica configuration and temperature permutation. This formulation is shown to facilitate the analysis of the…

化学物理 · 物理学 2017-12-20 Jianfeng Lu , Eric Vanden-Eijnden

We introduce the well-tempered ensemble (WTE) which is the biased ensemble sampled by well-tempered metadynamics when the energy is used as collective variable. WTE can be designed so as to have approximately the same average energy as the…

统计力学 · 物理学 2015-05-14 M. Bonomi , M. Parrinello

Monte Carlo simulations using entropic sampling to estimate the number of configurations of a given energy are a valuable alternative to traditional methods. We introduce {\it tomographic} entropic sampling, a scheme which uses multiple…

统计力学 · 物理学 2015-05-28 Ronald Dickman , A. G. Cunha-Netto

Biased sampling of collective variables is widely used to accelerate rare events in molecular simulations and to explore free energy surfaces. However, computational efficiency of these methods decreases with increasing number of collective…

化学物理 · 物理学 2017-04-05 Shalini Awasthi , Nisanth N. Nair

We review several parallel tempering schemes and examine their main ingredients for accuracy and efficiency. The present study covers two selection methods of temperatures and several choices for the exchange of replicas, including a recent…

统计力学 · 物理学 2015-06-16 A. Malakis , T. Papakonstantinou

The sampling problem lies at the heart of atomistic simulations and over the years many different enhanced sampling methods have been suggested towards its solution. These methods are often grouped into two broad families. On the one hand…

计算物理 · 物理学 2020-11-25 Michele Invernizzi , Pablo Miguel Piaggi , Michele Parrinello

We review a selection of methods for performing enhanced sampling in molecular dynamics simulations. We consider methods based on collective variable biasing and on tempering, and offer both historical and contemporary perspectives. In…

统计力学 · 物理学 2014-01-03 Cameron Abrams , Giovanni Bussi

Using normalizing flows and reweighting, Boltzmann Generators enable equilibrium sampling from a Boltzmann distribution, defined by an energy function and thermodynamic state. In this work, we introduce Thermodynamic Interpolation (TI),…

化学物理 · 物理学 2024-11-18 Selma Moqvist , Weilong Chen , Mathias Schreiner , Feliks Nüske , Simon Olsson

Importance sampling is a Monte Carlo method that introduces a proposal distribution to sample the space according to the target distribution. Yet calibration of the proposal distribution is essential to achieving efficiency, thus the resort…

统计计算 · 统计学 2022-06-17 Grégoire Aufort , Pierre Pudlo , Denis Burgarella

New sampling algorithms based on simulating continuous-time stochastic processes called piece-wise deterministic Markov processes (PDMPs) have shown considerable promise. However, these methods can struggle to sample from multi-modal or…

统计方法学 · 统计学 2022-05-31 Matthew Sutton , Robert Salomone , Augustin Chevallier , Paul Fearnhead

We present generalized-ensemble algorithms for isobaric-isothermal molecular simulations. In addition to the multibaric-multithermal algorithm and replica-exchange method for the isobaric-isothermal ensemble, which have already been…

统计力学 · 物理学 2010-04-14 Yoshiharu Mori , Yuko Okamoto

We describe a method to explore the configurational phase space of chemical systems. It is based on the nested sampling algorithm recently proposed by Skilling [Skilling J. (2004) In AIP Conference Proceedings, vol. 735, p. 395.; Skilling…

统计力学 · 物理学 2010-07-08 Livia B. Pártay , Albert P. Bartók , Gábor Csányi

In this paper we develop a new general Bayesian methodology that simultaneously estimates parameters of interest and the marginal likelihood of the model. The proposed methodology builds on Simulated Tempering, which is a powerful algorithm…

统计计算 · 统计学 2019-06-03 Biljana Jonoska Stojkova , David A. Campbell
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