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We present a new adaptive resolution technique for efficient particle-based multiscale molecular dynamics (MD) simulations. The presented approach is tailor-made for molecular systems where atomistic resolution is required only in spatially…

Soft Condensed Matter · Physics 2007-05-23 Matej Praprotnik , Luigi Delle Site , Kurt Kremer

For the study of complex synthetic and biological molecular systems by computer simulations one is still restricted to simple model systems or to by far too small time scales. To overcome this problem multiscale techniques are being…

Statistical Mechanics · Physics 2007-05-23 Matej Praprotnik , Kurt Kremer , Luigi Delle Site

We investigate the role of the thermodynamic (TD) force, as an essential and sufficient technical ingredient for an efficient and accurate adaptive resolution algorithm. Such a force applied in the coupling region of an adaptive resolution…

Computational Physics · Physics 2018-07-16 Christian Krekeler , Animesh Agarwal , Christoph Junghans , Matej Praprotnik , Luigi Delle Site

Simulations that couple different classical molecular models in an adaptive way by changing the number of degrees of freedom on the fly, are available within reasonably consistent theoretical frameworks. The same does not occur when it…

Soft Condensed Matter · Physics 2015-05-18 A. B. Poma , L. Delle Site

For simulation studies of (macro) molecular liquids it would be of significant interest to be able to adjust or increase the level of resolution within one region of space, while allowing for the free exchange of molecules between open…

Quantum mechanics based ab-initio molecular dynamics (MD) simulation schemes offer an accurate and direct means to monitor the time-evolution of materials. Nevertheless, the expensive and repetitive energy and force computations required in…

Materials Science · Physics 2014-10-14 Venkatesh Botu , Rampi Ramprasad

Molecular simulations of the forced unfolding and refolding of biomolecules or molecular complexes allow to gain important kinetic, structural and thermodynamic information about the folding process and the underlying energy landscape. In…

Soft Condensed Matter · Physics 2021-05-26 Marco Oestereich , Jürgen Gauss , Gregor Diezemann

During a crossover via a switching mechanism from one 2-body potential to another as might be applied in modeling (chemical) reactions in the vicinity of bond formation, energy violations would occur due to finite step size which determines…

Computational Physics · Physics 2007-05-23 Christopher G Jesudason

Molecular Dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively…

Biomolecules · Quantitative Biology 2023-07-20 Diego E. Kleiman , Hassan Nadeem , Diwakar Shukla

We propose an adaptive regularization scheme in a variational framework where a convex composite energy functional is optimized. We consider a number of imaging problems including denoising, segmentation and motion estimation, which are…

Computer Vision and Pattern Recognition · Computer Science 2017-03-01 Byung-Woo Hong , Ja-Keoung Koo , Hendrik Dirks , Martin Burger

Molecular dynamics (MD) is a powerful and popular tool for understanding the dynamical evolution of materials at the nano and mesoscopic scales. There are various flavors of MD ranging from the high fidelity albeit computationally expensive…

Computational Physics · Physics 2020-04-02 Rohit Batra , Subramanian Sankaranarayanan

Adaptive Molecular Resolution approaches in Molecular Dynamics are becoming relevant tools for the analysis of molecular liquids characterized by the interplay of different physical scales. The essential difference among these methods is in…

Computational Physics · Physics 2016-11-23 Jinglong Zhu , Rupert Klein , Luigi Delle Site

We present a fully adaptive multiresolution scheme for spatially two-dimensional, possibly degenerate reaction-diffusion systems, focusing on combustion models and models of pattern formation and chemotaxis in mathematical biology.…

Numerical Analysis · Mathematics 2008-07-10 Mostafa Bendahmane , Raimund Bürger , Ricardo Ruiz Baier , Kai Schneider

Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is in contrast to most other machine learning domains, where specialist approaches are…

Machine Learning · Computer Science 2026-01-30 Max Eissler , Tim Korjakow , Stefan Ganscha , Oliver T. Unke , Klaus-Robert Müller , Stefan Gugler

We propose an open-boundary molecular dynamics method in which an atomistic system is in contact with an infinite particle reservoir at constant temperature, volume and chemical potential. In practice, following the Hamiltonian adaptive…

Statistical Mechanics · Physics 2020-06-24 Maziar Heidari , Kurt Kremer , Ramin Golestanian , Raffaello Potestio , Robinson Cortes-Huerto

-Molecular simulations allow the study of properties and interactions of molecular systems. This article presents an improved version of the Adaptive Resolution Scheme that links two systems having atomistic (also called fine-grained) and…

Computational Engineering, Finance, and Science · Computer Science 2017-08-01 Iuliana Marin , Virgil Tudose , Anton Hadar , Nicolae Goga , Andrei Doncescu

Diffusive molecular dynamics is a novel model for materials with atomistic resolution that can reach diffusive time scales. The main ideas of diffusive molecular dynamics are to first minimize an approximate variational Gaussian free energy…

Numerical Analysis · Mathematics 2015-06-09 Gideon Simpson , Mitchell Luskin , David J. Srolovitz

Using a coarse molecular-dynamics (CMD) approach with an appropriate choice of coarse variable (order parameter), we map the underlying effective free-energy landscape for the melting of a crystalline solid. Implementation of this approach…

Materials Science · Physics 2009-11-11 Miguel A. Amat , Ioannis G. Kevrekidis , Dimitrios Maroudas

Multi-objective evolutionary algorithms (MOEAs) are widely used to solve multi-objective optimization problems. The algorithms rely on setting appropriate parameters to find good solutions. However, this parameter tuning could be very…

Neural and Evolutionary Computing · Computer Science 2022-11-18 Remco Coppens , Robbert Reijnen , Yingqian Zhang , Laurens Bliek , Berend Steenhuisen

Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments. While notable progress has been made, the capabilities of differentiable physics solvers remain limited. We…

Machine Learning · Computer Science 2020-07-07 Yi-Ling Qiao , Junbang Liang , Vladlen Koltun , Ming C. Lin
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