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Related papers: Exploring High Dimensional Free Energy Landscapes:…

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A simple, efficient, and accurate method is proposed to map multi-dimensional free energy landscapes. The method combines the temperature-accelerated molecular dynamics (TAMD) proposed in [Maragliano & Vanden-Eijnden, Chem. Phys. Lett. 426,…

Computational Physics · Physics 2009-11-13 Luca Maragliano , Eric Vanden-Eijnden

Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for molecular sampling because they are often slower than molecular…

Nested sampling is a Bayesian sampling technique developed to explore probability distributions lo- calised in an exponentially small area of the parameter space. The algorithm provides both posterior samples and an estimate of the evidence…

Biomolecules · Quantitative Biology 2015-03-17 Nikolas S. Burkoff , Csilla Varnai , Stephen A. Wells , David L. Wild

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

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…

Chemical Physics · Physics 2018-10-29 Yi Isaac Yang , Haiyang Niu , Michele Parrinello

TAPAS is a novel adaptive sampling method for the softmax model. It uses a two pass sampling strategy where the examples used to approximate the gradient of the partition function are first sampled according to a squashed population…

Machine Learning · Computer Science 2017-07-17 Yu Bai , Sally Goldman , Li Zhang

Calculating free energies is an important and notoriously difficult task for molecular simulations. The rapid increase in computational power has made it possible to probe increasingly complex systems, yet extracting accurate free energies…

Chemical Physics · Physics 2014-07-29 Viveca Lindahl , Jack Lidmar , Berk Hess

Collective variable (CV) or order parameter based enhanced sampling algorithms have achieved great success due to their ability to efficiently explore the rough potential energy landscapes of complex systems. However, the degeneracy of…

Chemical Physics · Physics 2018-07-11 Jing Zhang , Ming Chen

Sampling the free energy surface, namely, the distribution of collective variables (CVs), is a crucial problem in statistical physics, as it underpins a better understanding of chemical reactions and conformational transitions. Traditional…

Machine Learning · Computer Science 2026-05-04 Zichen Liu , Tiejun Li

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

A popular way to accelerate the sampling of rare events in molecular dynamics simulations is to introduce a potential that increases the fluctuations of selected collective variables. For this strategy to be successful, it is critical to…

Computational Physics · Physics 2021-01-19 Luigi Bonati

The free energetics of water density fluctuations in bulk water, at interfaces, and in hydrophobic confinement inform the hydration of hydrophobic solutes as well as their interactions and assembly. The characterization of such free…

Statistical Mechanics · Physics 2018-03-15 Erte Xi , Sean M. Marks , Suruchi Fialoke , Amish J. Patel

We demonstrate how a prior assumption of smoothness can be used to enhance the reconstruction of free energy profiles from multiple umbrella sampling simulations using the Bayesian Gaussian process regression approach. The method we derive…

Statistical Mechanics · Physics 2014-07-25 Noam Bernstein , Thomas Stecher , Gábor Csányi

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

Many biological processes occur on time scales longer than those accessible to molecular dynamics simulations. Identifying collective variables (CVs) and introducing an external potential to accelerate them is a popular approach to address…

Computational Physics · Physics 2024-10-24 Enrico Trizio , Andrea Rizzi , Pablo M. Piaggi , Michele Invernizzi , Luigi Bonati

The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation. Recently, diffusion models such as BioEmu have emerged as powerful equilibrium samplers that…

We describe a method that focuses sampling effort on a user-defined selection of a large system, which can lead to substantial decreases in computational effort by speeding up the calculation of nonbonded interactions. A naive approach can…

Statistical Mechanics · Physics 2023-08-28 Joshua Fass , Forrest York , Matthew Wittmann , Joseph Kaus , Yutong Zhao

Metadynamics, a member of the `flat histogram' class of advanced sampling algorithms, has been widely used in molecular simulations to drive the exploration of states separated by high free energy barriers and promote comprehensive sampling…

Computational Physics · Physics 2021-09-13 Shanghui Huang , Michael J. Quevillon , Ernesto C. Cortés-Morales , Jonathan K. Whitmer

Stochastic nonlinear dynamical systems can undergo rapid transitions relative to the change in their forcing, for example due to the occurrence of multiple equilibrium solutions for a specific interval of parameters. In this paper, we…

Data Analysis, Statistics and Probability · Physics 2020-11-12 S. Baars , D. Castellana , F. W. Wubs , H. A. Dijkstra

A leading goal for climate science and weather risk management is to accurately model both the physics and statistics of extreme events. These two goals are fundamentally at odds: the higher a computational model's resolution, the more…

Atmospheric and Oceanic Physics · Physics 2024-02-06 Justin Finkel , Paul A. O'Gorman