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In complex systems with many degrees of freedom such as spin glass and biomolecular systems, conventional simulations in canonical ensemble suffer from the quasi-ergodicity problem. A simulation in generalized ensemble performs a random…

Statistical Mechanics · Physics 2008-06-24 Y. Okamoto

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…

Statistical Mechanics · Physics 2010-04-14 Yoshiharu Mori , Yuko Okamoto

Population annealing is a powerful tool for large-scale Monte Carlo simulations. We adapt this method to molecular dynamics simulations and demonstrate its excellent accelerating effect by simulating the folding of a short peptide commonly…

Computational Physics · Physics 2019-02-27 Henrik Christiansen , Martin Weigel , Wolfhard Janke

The determination of efficient collective variables is crucial to the success of many enhanced sampling methods. As inspired by previous discrimination approaches, we first collect a set of data from the different metastable basins. The…

Computational Physics · Physics 2026-03-03 Enrico Trizio , Michele Parrinello

All-atom molecular dynamics (MD) computer simulations are a valuable tool for characterizing the conformational ensembles of intrinsically disordered proteins (IDPs). IDP conformational ensembles are highly heterogeneous and contain…

Chemical Physics · Physics 2025-05-06 Jaya Krishna Koneru , Korey M. Reid , Paul Robustelli

In complex systems with many degrees of freedom such as peptides and proteins there exist a huge number of local-minimum-energy states. Conventional simulations in the canonical ensemble are of little use, because they tend to get trapped…

Statistical Mechanics · Physics 2007-05-23 Ayori Mitsutake , Yuji Sugita , Yuko Okamoto

We present an efficient sampling method for computing a partition function and accelerating configuration sampling. The method performs a random walk in the $\lambda$ space, with $\lambda$ being any thermodynamic variable that characterizes…

Computational Physics · Physics 2010-03-02 Cheng Zhang , Jianpeng Ma

Molecular simulations are playing an ever increasing role, finding applications in fields as varied as physics, chemistry, biology and material science. However, many phenomena of interest take place on time scales that are out of reach of…

Computational Physics · Physics 2021-10-07 Michele Invernizzi

Integrated tempering sampling (ITS) method is an approach to enhance the sampling over a broad range of energies and temperatures in computer simulations. In this paper, a new version of integrated tempering sampling method is proposed. In…

Computational Physics · Physics 2015-06-15 Peng Zhao , Li Jiang Yang , Yi Qin Gao , Zhong-Yuan Lu

Biased sampling methods such as the Temperature Accelerated Sliced Sampling (TASS), which can explore high dimensional collective variable (CV) space, is of great interest in free energy calculations. Such methods can efficiently sample…

Computational Physics · Physics 2020-10-06 Abhinav Gupta , Shivani Verma , Nisanth N. Nair

Biased enhanced sampling methods utilizing collective variables (CVs) are powerful tools for sampling conformational ensembles. Due to high intrinsic dimensions, efficiently generating conformational ensembles for complex systems requires…

Machine Learning · Computer Science 2023-12-19 Yikai Liu , Tushar K. Ghosh , Guang Lin , Ming Chen

Understanding protein conformational dynamics is essential for elucidating biological function but remains challenging due to the wide range of timescales and the complexity of collective motions. Enhanced sampling methods overcome…

Statistical Mechanics · Physics 2026-05-11 Souvik Mondal , Michael A. Sauer , Matthias Heyden

Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastability. Simulating transition paths linking one metastable state…

Machine Learning · Statistics 2023-02-02 Tony Lelièvre , Geneviève Robin , Inass Sekkat , Gabriel Stoltz , Gabriel Victorino Cardoso

The study of the rare transitions that take place between long lived metastable states is a major challenge in molecular dynamics simulations. Many of the methods suggested to address this problem rely on the identification of the slow…

Chemical Physics · Physics 2023-06-07 Dhiman Ray , Enrico Trizio , Michele Parrinello

The effectiveness of a new algorithm, parallel tempering, is studied for numerical simulations of biological molecules. These molecules suffer from a rough energy landscape. The resulting slowing down in numerical simulations is overcome by…

Chemical Physics · Physics 2009-10-30 Ulrich H. E. Hansmann

New hybrid Molecular Dynamics-Monte Carlo methods are proposed to increase the efficiency of constant-pressure simulations. Two variations of the isobaric Molecular Dynamics component of the algorithms are considered. In the first, we use…

Soft Condensed Matter · Physics 2009-11-07 Roland Faller , Juan J. de Pablo

The phenomenon of solidification of a substance from its liquid phase is of the greatest practical and theoretical importance, and atomistic simulations can provide precious information towards its understanding and control. Unfortunately,…

Soft Condensed Matter · Physics 2021-03-25 Tarak Karmakar , Michele Invernizzi , Valerio Rizzi , Michele Parrinello

Massively-parallel molecular dynamics simulation is applied to systems containing electrolytes, vapour-liquid interfaces, and biomolecules in contact with water-oil interfaces. Novel molecular models of alkali halide salts are presented and…

In the absence of impurities and boundary effects, first order phase transitions are initiated by the nucleation of critical bubbles. In thermally driven transitions many systems can remain metastable for an extended time, possibly tens of…

High Energy Physics - Lattice · Physics 2025-02-21 Jaakko Hällfors , Kari Rummukainen

While gradient-based discrete samplers are effective in sampling from complex distributions, they are susceptible to getting trapped in local minima, particularly in high-dimensional, multimodal discrete distributions, owing to the…

Machine Learning · Statistics 2025-05-21 Luxu Liang , Yuhang Jia , Feng Zhou