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Metropolis simulations of all-atom models of peptides (i.e. small proteins) are considered. Inspired by the funnel picture of Bryngelson and Wolyness, a transformation of the updating probabilities of the dihedral angles is defined, which…

Statistical Mechanics · Physics 2009-11-10 Bernd A. Berg

We derived a number of numerical methods to treat biomolecular systems with multiple time scales. Based on the splitting of the operators associated with the slow-varying and fast-varying forces, new multiple time-stepping (MTS) methods are…

Numerical Analysis · Mathematics 2015-01-15 Chao Liang , Xiaolan Yuan , Xiantao Li

The high accuracy exhibited by biological information transcription processes is due to kinetic proofreading, i.e., by a mechanism which reduces the error rate of the information-handling process by driving it out of equilibrium. We provide…

Statistical Mechanics · Physics 2015-06-18 Riccardo Rao , Luca Peliti

We propose an adaptive biasing algorithm aimed at enhancing the sampling of multimodal measures by Langevin dynamics. The underlying idea consists in generalizing the standard adaptive biasing force method commonly used in conjunction with…

Analysis of PDEs · Mathematics 2010-08-23 Chris Chipot , Tony Lelièvre

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…

Statistical Mechanics · Physics 2021-02-03 Fahim Faizi , Pedro J. Buigues , George Deligiannidis , Edina Rosta

We compute the thermodynamic properties of the glass phase in a binary mixture of soft spheres. Our approach is a generalization to mixtures of the replica strategy, recently proposed by Mezard and Parisi, providing a first principle…

Condensed Matter · Physics 2009-10-31 Barbara Coluzzi , Marc Mezard , Giorgio Parisi , Paolo Verrocchio

In particle-in-cell simulations, excessive or even unfeasible computational demands can be caused by the growth of the number of particles in the course of prolific ionization or cascaded pair production due to the effects of quantum…

Computational Physics · Physics 2021-01-25 A. Muraviev , A. Bashinov , E. Efimenko , V. Volokitin , I. Meyerov , A. Gonoskov

We present an Adaptively Biased Molecular Dynamics (ABMD) method for the computation of the free energy surface of a reaction coordinate using non-equilibrium dynamics. The ABMD method belongs to the general category of umbrella sampling…

Materials Science · Physics 2008-04-16 Volodymyr Babin , Christopher Roland , Celeste Sagui

We investigate a generic, parallel replica-exchange framework for Monte Carlo simulations based on the Wang-Landau method. To demonstrate its advantages and general applicability for massively parallel simulations of complex systems, we…

Computational Physics · Physics 2015-06-22 Thomas Vogel , Ying Wai Li , Thomas Wüst , David P. Landau

This letter highlights the entropy exchange phenomenon in a coupled binary inter-correlating system following Haldane's non-linear statistical correlation. A unique coupling between a classical and a quantum-like system at the marginal…

Statistical Mechanics · Physics 2025-05-22 Projesh Kumar Roy

Bayesian inference for factorial hidden Markov models is challenging due to the exponentially sized latent variable space. Standard Monte Carlo samplers can have difficulties effectively exploring the posterior landscape and are often…

Computation · Statistics 2019-02-28 Kaspar Märtens , Michalis K Titsias , Christopher Yau

The ability of widely used sampling methods, such as molecular dynamics or Monte Carlo, to explore complex free energy landscapes is severely hampered by the presence of kinetic bottlenecks. A large number of solutions have been proposed to…

Statistical Mechanics · Physics 2014-08-29 Omar Valsson , Michele Parrinello

Empirical force fields employed in molecular dynamics simulations of complex systems can be optimised to reproduce experimentally determined structural and thermodynamic properties. In contrast, experimental knowledge about the rates of…

Statistical Mechanics · Physics 2022-07-12 P. G. Bolhuis , Z. F. Brotzakis , B. G. Keller

Analyzing the morphology of cells in microscopy images can provide insights into the mechanism of compounds or the function of genes. Addressing this task requires methods that can not only extract biological information from the images,…

Machine Learning · Computer Science 2021-12-07 Siqi Wang , Manyuan Lu , Nikita Moshkov , Juan C. Caicedo , Bryan A. Plummer

Biomolecular force fields have been traditionally derived based on a mixture of reference quantum chemistry data and experimental information obtained on small fragments. However, the possibility to run extensive molecular dynamics…

Computational Physics · Physics 2020-06-19 Thorben Fröhlking , Mattia Bernetti , Nicola Calonaci , Giovanni Bussi

Many applications require that we learn the parameters of a model from data. EM is a method used to learn the parameters of probabilistic models for which the data for some of the variables in the models is either missing or hidden. There…

Machine Learning · Computer Science 2013-01-30 Luis E. Ortiz , Leslie Pack Kaelbling

Enhanced sampling methods are indispensable in computational physics and chemistry, where atomistic simulations cannot exhaustively sample the high-dimensional configuration space of dynamical systems due to the sampling problem. A class of…

Chemical Physics · Physics 2024-04-04 Jakub Rydzewski , Ming Chen , Tushar K. Ghosh , Omar Valsson

The Empirical Valence Bond (EVB) method offers a suitable framework to obtain reactive potentials through the coupling of non-reactive force fields. However, most of the implemented functional forms for the coupling terms depend on complex…

Chemical Physics · Physics 2021-01-27 Ivan Scivetti , Kakali Sen , Alin M. Elena , Ilian Todorov

We introduce and illustrate a number of performance measures for rare-event sampling methods. These measures are designed to be of use in a variety of expanded ensemble techniques including parallel tempering as well as infinite and partial…

Statistical Mechanics · Physics 2015-06-23 J. D. Doll , Paul Dupuis

Machine learning methods provide a general framework for automatically finding and representing the essential characteristics of simulation data. This task is particularly crucial in enhanced sampling simulations. There we seek a few…

Chemical Physics · Physics 2021-07-07 Jakub Rydzewski , Omar Valsson