Multiscale modeling of emergent materials : biological and soft matter

T. Murtola, A. Bunker, I. Vattulainen, M. Deserno, M.E.J. Karttunen

    Research output: Contribution to journalArticleAcademicpeer-review

    193 Citations (Scopus)

    Abstract

    In this review, we focus on four current related issues in multiscale modeling of soft and biological matter. First, we discuss how to use structural information from detailed models (or experiments) to construct coarse-grained ones in a hierarchical and systematic way. This is discussed in the context of the so-called Henderson theorem and the inverse Monte Carlo method of Lyubartsev and Laaksonen. In the second part, we take a different look at coarse graining by analyzing conformations of molecules. This is done by the application of self-organizing maps, i.e., a neural network type approach. Such an approach can be used to guide the selection of the relevant degrees of freedom. Then, we discuss technical issues related to the popular dissipative particle dynamics (DPD) method. Importantly, the potentials derived using the inverse Monte Carlo method can be used together with the DPD thermostat. In the final part we focus on solvent-free modeling which offers a different route to coarse graining by integrating out the degrees of freedom associated with solvent.
    Original languageEnglish
    Pages (from-to)1869-1892
    JournalPhysical Chemistry Chemical Physics
    Volume11
    Issue number12
    DOIs
    Publication statusPublished - 2009

    Fingerprint

    Biological materials
    Monte Carlo method
    Monte Carlo methods
    degrees of freedom
    Thermostats
    thermostats
    organizing
    Self organizing maps
    Conformations
    theorems
    routes
    Neural networks
    Molecules
    molecules
    Experiments

    Cite this

    Murtola, T., Bunker, A., Vattulainen, I., Deserno, M., & Karttunen, M. E. J. (2009). Multiscale modeling of emergent materials : biological and soft matter. Physical Chemistry Chemical Physics, 11(12), 1869-1892. https://doi.org/10.1039/B818051B
    Murtola, T. ; Bunker, A. ; Vattulainen, I. ; Deserno, M. ; Karttunen, M.E.J. / Multiscale modeling of emergent materials : biological and soft matter. In: Physical Chemistry Chemical Physics. 2009 ; Vol. 11, No. 12. pp. 1869-1892.
    @article{4bce3bb8ebb24e83bc10f01dd4deccd8,
    title = "Multiscale modeling of emergent materials : biological and soft matter",
    abstract = "In this review, we focus on four current related issues in multiscale modeling of soft and biological matter. First, we discuss how to use structural information from detailed models (or experiments) to construct coarse-grained ones in a hierarchical and systematic way. This is discussed in the context of the so-called Henderson theorem and the inverse Monte Carlo method of Lyubartsev and Laaksonen. In the second part, we take a different look at coarse graining by analyzing conformations of molecules. This is done by the application of self-organizing maps, i.e., a neural network type approach. Such an approach can be used to guide the selection of the relevant degrees of freedom. Then, we discuss technical issues related to the popular dissipative particle dynamics (DPD) method. Importantly, the potentials derived using the inverse Monte Carlo method can be used together with the DPD thermostat. In the final part we focus on solvent-free modeling which offers a different route to coarse graining by integrating out the degrees of freedom associated with solvent.",
    author = "T. Murtola and A. Bunker and I. Vattulainen and M. Deserno and M.E.J. Karttunen",
    year = "2009",
    doi = "10.1039/B818051B",
    language = "English",
    volume = "11",
    pages = "1869--1892",
    journal = "Physical Chemistry Chemical Physics",
    issn = "1463-9076",
    publisher = "Royal Society of Chemistry",
    number = "12",

    }

    Murtola, T, Bunker, A, Vattulainen, I, Deserno, M & Karttunen, MEJ 2009, 'Multiscale modeling of emergent materials : biological and soft matter', Physical Chemistry Chemical Physics, vol. 11, no. 12, pp. 1869-1892. https://doi.org/10.1039/B818051B

    Multiscale modeling of emergent materials : biological and soft matter. / Murtola, T.; Bunker, A.; Vattulainen, I.; Deserno, M.; Karttunen, M.E.J.

    In: Physical Chemistry Chemical Physics, Vol. 11, No. 12, 2009, p. 1869-1892.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - Multiscale modeling of emergent materials : biological and soft matter

    AU - Murtola, T.

    AU - Bunker, A.

    AU - Vattulainen, I.

    AU - Deserno, M.

    AU - Karttunen, M.E.J.

    PY - 2009

    Y1 - 2009

    N2 - In this review, we focus on four current related issues in multiscale modeling of soft and biological matter. First, we discuss how to use structural information from detailed models (or experiments) to construct coarse-grained ones in a hierarchical and systematic way. This is discussed in the context of the so-called Henderson theorem and the inverse Monte Carlo method of Lyubartsev and Laaksonen. In the second part, we take a different look at coarse graining by analyzing conformations of molecules. This is done by the application of self-organizing maps, i.e., a neural network type approach. Such an approach can be used to guide the selection of the relevant degrees of freedom. Then, we discuss technical issues related to the popular dissipative particle dynamics (DPD) method. Importantly, the potentials derived using the inverse Monte Carlo method can be used together with the DPD thermostat. In the final part we focus on solvent-free modeling which offers a different route to coarse graining by integrating out the degrees of freedom associated with solvent.

    AB - In this review, we focus on four current related issues in multiscale modeling of soft and biological matter. First, we discuss how to use structural information from detailed models (or experiments) to construct coarse-grained ones in a hierarchical and systematic way. This is discussed in the context of the so-called Henderson theorem and the inverse Monte Carlo method of Lyubartsev and Laaksonen. In the second part, we take a different look at coarse graining by analyzing conformations of molecules. This is done by the application of self-organizing maps, i.e., a neural network type approach. Such an approach can be used to guide the selection of the relevant degrees of freedom. Then, we discuss technical issues related to the popular dissipative particle dynamics (DPD) method. Importantly, the potentials derived using the inverse Monte Carlo method can be used together with the DPD thermostat. In the final part we focus on solvent-free modeling which offers a different route to coarse graining by integrating out the degrees of freedom associated with solvent.

    U2 - 10.1039/B818051B

    DO - 10.1039/B818051B

    M3 - Article

    VL - 11

    SP - 1869

    EP - 1892

    JO - Physical Chemistry Chemical Physics

    JF - Physical Chemistry Chemical Physics

    SN - 1463-9076

    IS - 12

    ER -