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      Soft Matter under Pressure: Pushing Particle–Field Molecular Dynamics to the Isobaric Ensemble

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          Abstract

          Hamiltonian hybrid particle–field molecular dynamics is a computationally efficient method to study large soft matter systems. In this work, we extend this approach to constant-pressure (NPT) simulations. We reformulate the calculation of internal pressure from the density field by taking into account the intrinsic spread of the particles in space, which naturally leads to a direct anisotropy in the pressure tensor. The anisotropic contribution is crucial for reliably describing the physics of systems under pressure, as demonstrated by a series of tests on analytical and monatomic model systems as well as realistic water/lipid biphasic systems. Using Bayesian optimization, we parametrize the field interactions of phospholipids to reproduce the structural properties of their lamellar phases, including area per lipid, and local density profiles. The resulting model excels in providing pressure profiles in qualitative agreement with all-atom modeling, and surface tension and area compressibility in quantitative agreement with experimental values, indicating the correct description of long-wavelength undulations in large membranes. Finally, we demonstrate that the model is capable of reproducing the formation of lipid droplets inside a lipid bilayer.

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          Most cited references64

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          GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

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            Canonical sampling through velocity rescaling

            The authors 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 that, in spite of its stochastic nature, a quantity can still be defined that remains constant during the evolution. In numerical applications this quantity can be used to measure the accuracy of the sampling. The authors illustrate the properties of this new method on Lennard-Jones and TIP4P water models in the solid and liquid phases. Its performance is excellent and largely independent of the thermostat parameter also with regard to the dynamic properties.
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              Molecular dynamics with coupling to an external bath

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                Author and article information

                Journal
                J Chem Inf Model
                J Chem Inf Model
                ci
                jcisd8
                Journal of Chemical Information and Modeling
                American Chemical Society
                1549-9596
                1549-960X
                28 March 2023
                10 April 2023
                : 63
                : 7
                : 2207-2217
                Affiliations
                [1]Hylleraas Centre for Quantum Molecular Sciences and Department of Chemistry, University of Oslo , P.O. Box 1033 Blindern, 0315 Oslo, Norway
                Author notes
                Author information
                https://orcid.org/0000-0002-1922-7796
                https://orcid.org/0000-0003-4244-4876
                https://orcid.org/0000-0002-8620-4885
                https://orcid.org/0000-0003-2266-5399
                Article
                10.1021/acs.jcim.3c00186
                10091448
                36976890
                56d5bd61-4309-4845-91db-9f9618ffb2ea
                © 2023 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 06 February 2023
                Funding
                Funded by: Deutsche Forschungsgemeinschaft, doi 10.13039/501100001659;
                Award ID: 233630050
                Funded by: NOTUR, doi NA;
                Award ID: NN4654K
                Funded by: Norges Forskningsråd, doi 10.13039/501100005416;
                Award ID: 262695
                Categories
                Article
                Custom metadata
                ci3c00186
                ci3c00186

                Computational chemistry & Modeling
                Computational chemistry & Modeling

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