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Configurational entropy is an important factor in the free energy change of many macromolecular recognition and binding processes, and has been intensively studied. Despite great progresses that have been made, the global sampling remains…

生物物理 · 物理学 2012-12-04 Wenzhao Li , Kai Wang , Suyan Tian , Pu Tian

We present a novel Monte Carlo algorithm which enhances equilibrization of low-temperature simulations and allows sampling of configurations over a large range of energies. The method is based on a non-Boltzmann probability weight factor…

凝聚态物理 · 物理学 2009-10-30 Ulrich H. E. Hansmann , Yuko Okamoto

Spontaneous structural rearrangements play a central role in the organization and function of complex biomolecular systems. In principle, physics-based computer simulations like Molecular Dynamics (MD) enable us to investigate these…

量子物理 · 物理学 2026-03-19 Danial Ghamari , Philipp Hauke , Roberto Covino , Pietro Faccioli

Various non-trivial spaces are becoming popular for embedding structured data such as graphs, texts, or images. Following spherical and hyperbolic spaces, more general product spaces have been proposed. However, searching for the best…

机器学习 · 计算机科学 2022-04-11 Kirill Shevkunov , Liudmila Prokhorenkova

We propose two efficient algorithms for configurational sampling of systems with rough energy landscape. The first one is a new method for the determination of the multicanonical weight factor. In this method a short replica-exchange…

统计力学 · 物理学 2009-10-31 Yuji Sugita , Yuko Okamoto

We previously introduced a conformational sampling method, a multi-dimensional virtual-system coupled molecular dynamics (mD-VcMD), to enhance conformational sampling of a biomolecular system by computer simulations. Here, we present a new…

Sampling from flat energy or density distributions has proven useful in equilibrating complex systems with large energy barriers. Several thermostats and barostats are presented to sample these flat distributions by molecular dynamics.…

计算物理 · 物理学 2015-06-12 Cheng Zhang , Michael W. Deem

The computer simulation of many molecular processes is complicated by long time scales caused by rare transitions between long-lived states. Here, we propose a new approach to simulate such rare events, which combines transition path…

计算物理 · 物理学 2023-03-23 Sebastian Falkner , Alessandro Coretti , Christoph Dellago

Atomistic modelling of phase transitions, chemical reactions, or other rare events that involve overcoming high free energy barriers usually entails prohibitively long simulation times. Introducing a bias potential as a function of an…

计算物理 · 物理学 2019-11-06 Federico Giberti , Bingqing Cheng , Gareth Aneurin Tribello , Michele Ceriotti

Numerous methods have been proposed to adapt a pre-trained foundational CLIP model for few-shot classification. As CLIP is trained on a large corpus, it generalises well through adaptation to few-shot classification. In this work, we…

计算机视觉与模式识别 · 计算机科学 2024-09-18 Alexey Kravets , Vinay Namboodiri

We present an investigation into data selection methods for the efficient sampling of configuration space as applied to the development of inter-atomic potentials for scale bridging in molecular dynamics (MD) simulations. This investigation…

计算物理 · 物理学 2021-08-04 Jan Finkbeiner , Samuel Tovey , Christian Holm

We propose here some new sampling algorithms for Path Sampling in the case when stochastic dynamics are used. In particular, we present a new proposal function for equilibrium sampling of paths with a Monte-Carlo dynamics (the so-called…

统计力学 · 物理学 2009-11-11 Gabriel Stoltz

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…

统计力学 · 物理学 2023-08-28 Joshua Fass , Forrest York , Matthew Wittmann , Joseph Kaus , Yutong Zhao

One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes associated with either large system size or long timescales. To overcome this bottleneck, we…

生物大分子 · 定量生物学 2018-07-09 Zahra Shamsi , Kevin J. Cheng , Diwakar Shukla

We present a technique for optimizing hundreds of thousands of variational parameters in variational quantum Monte Carlo. By introducing iterative Krylov subspace solvers and by multiplying by the Hamiltonian and overlap matrices as they…

强关联电子 · 物理学 2013-05-30 Eric Neuscamman , C. J. Umrigar , Garnet Kin-Lic Chan

Enhanced sampling algorithms have emerged as powerful methods to extend the utility of molecular dynamics simulations and allow the sampling of larger portions of the configuration space of complex systems in a given amount of simulation…

统计力学 · 物理学 2022-12-19 Jérôme Hénin , Tony Lelièvre , Michael R. Shirts , Omar Valsson , Lucie Delemotte

We present an unsupervised data processing workflow that is specifically designed to obtain a fast conformational clustering of long molecular dynamics simulation trajectories. In this approach we combine two dimensionality reduction…

化学物理 · 物理学 2023-08-09 Simon Hunkler , Kay Diederichs , Oleksandra Kukharenko , Christine Peter

In this article, we focus on the sampling of the configurational Gibbs-Boltzmann distribution, that is, the calculation of averages of functions of the position coordinates of a molecular $N$-body system modelled at constant temperature. We…

数值分析 · 数学 2025-04-30 Benedict Leimkuhler , Charles Matthews

We present a new molecular dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random factor. The algorithm is formally justified and it is shown…

统计力学 · 物理学 2008-03-31 Giovanni Bussi , Davide Donadio , Michele Parrinello

In observational studies, the assumption of sufficient overlap (positivity) is fundamental for the identification and estimation of causal effects. Failing to account for this assumption yields inaccurate and potentially infeasible…

统计方法学 · 统计学 2025-04-07 Jaehyuk Jang , Suehyun Kim , Kwonsang Lee