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Polymer chains in colloid-polymer mixtures can be coarse-grained by replacing them with single soft particles interacting via effective polymer-polymer and polymer-colloid pair potentials. Here we describe in detail how Ornstein-Zernike…

Soft Condensed Matter · Physics 2007-05-23 P. G. Bolhuis , A. A. Louis

The current capacity of computers makes it possible to perform simulations of small systems with portable, explicit-solvent potentials achieving high degree of accuracy. However, simplified models must be employed to exploit the behaviour…

Biomolecules · Quantitative Biology 2015-06-18 R. Capelli , C. Paissoni , P. Sormanni , G. Tiana

The accuracy of atomistic simulations depends on the precision of force fields. Traditional numerical methods often struggle to optimize the empirical force field parameters for reproducing target properties. Recent approaches rely on…

We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of data driven statistical fits of recent machine learned…

Chemical Physics · Physics 2020-10-26 Alice Allen , Gábor Csányi , Geneviève Dusson , Christoph Ortner

We develop coarse-grained force fields for poly (vinyl alcohol) and poly (acrylic acid) oligomers. In both cases, one monomer is mapped onto a coarse-grained bead. The new force fields are designed to match structural properties such as…

Soft Condensed Matter · Physics 2009-10-31 Dirk Reith , Hendrik Meyer , Florian Mueller-Plathe

We map dilute or semi-dilute solutions of non-intersecting polymer chains onto a fluid of ``soft'' particles interacting via a concentration dependent effective pair potential, by inverting the pair distribution function of the centers of…

Soft Condensed Matter · Physics 2009-10-31 A. A. Louis , P. G. Bolhuis , J. P. Hansen , E. J. Meijer

Central idea: To obtain the interaction potential using the inverse scattering method, we have employed the Physics-Informed Machine Learning (PIML) approach. In this framework, the machine learning algorithm is guided by the underlying…

A new potential of mean force is proposed for colloidal dispersions, which is obtained from coarse grained, pair interactions between colloidal particles formed by the explicit grouping of particles that are themselves groups of atoms and…

Soft Condensed Matter · Physics 2015-10-06 K. A. Terrón-Mejía , R. López-Rendón , A. Gama Goicochea

We model the flow behaviour of dense melts of flexible and semiflexible ring polymers in the presence of walls using a hybrid multiscale approach. Specifically, we perform molecular dynamics simulations and apply the Irving-Kirkwood formula…

Numerical Analysis · Mathematics 2026-05-25 Ranajay Datta , Mária Lukáčová-Medviďová , Andreas Schömer , Peter Virnau

Mono- and poly-disperse melts of oligomers (average length 10 monomers) of trans-1,4-polyisoprene are simulated in full atomistic detail. The force-field is developed by means of a mixture of ab initio quantum-chemistry and an automatic…

Soft Condensed Matter · Physics 2009-10-31 Roland Faller , Florian Mueller-Plathe , Manolis Doxastakis , Doros Theodorou

We present a promising coarse-graining strategy for linking micro- and mesoscales of soft matter systems. The approach is based on effective pairwise interaction potentials obtained from detailed atomistic molecular dynamics (MD)…

Soft Condensed Matter · Physics 2007-05-23 A. P. Lyubartsev , M. Karttunen , I. Vattulainen , A. Laaksonen

We present a theoretical approach to scale the artificially fast dynamics of simulated coarse-grained polymer liquids down to its realistic value. As coarse-graining affects entropy and dissipation, two factors enter the rescaling:…

Soft Condensed Matter · Physics 2015-05-27 I. Y. Lyubimov , J. McCarty , A. Clark , M. G. Guenza

Mesoscopic molecular dynamics simulations are used to determine the large scale structure of several binary polymer mixtures of various chemical architecture, concentration, and thermodynamic conditions. By implementing an analytical…

Soft Condensed Matter · Physics 2010-04-05 J. McCarty , I. Y. Lyubimov , M. G. Guenza

The determination of potentials of mean force for solute insertion in a membrane by means of all-atom molecular dynamics simulations is often hampered by sampling issues. A multiscale approach to conformational sampling was recently…

Chemical Physics · Physics 2017-10-09 Roberto Menichetti , Kurt Kremer , Tristan Bereau

Several recently proposed semi--automatic and fully--automatic coarse--graining schemes for polymer simulations are discussed. All these techniques derive effective potentials for multi--atom units or super--atoms from atomistic…

Soft Condensed Matter · Physics 2007-05-23 Roland Faller

Solutions of interacting linear polymers are mapped onto a system of ``soft'' spherical particles interacting via an effective pair potential. This coarse-graining reduces the individual monomer-level description to a problem involving only…

Soft Condensed Matter · Physics 2007-05-23 V. Krakoviack , J. P. Hansen , A. A. Louis

Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical…

Polymers are a versatile class of materials with widespread industrial applications. Advanced computational tools could revolutionize their design, but their complex, multi-scale nature poses significant modeling challenges. Conventional…

Obtaining a rigorous and reliable method for linking computer simulations of polymer blends and composites at different length scales of interest is a highly desirable goal in soft matter physics. In this paper a multiscale modeling…

Soft Condensed Matter · Physics 2015-05-19 J. McCarty , M. G. Guenza

We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving…

Computational Physics · Physics 2017-01-04 Huan Lei , Xiu Yang , Zhen Li , George Karniadakis
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