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Handbook of Materials Modeling pp 1–12Cite as

Data-Driven Methods in Multiscale Modeling of Soft Matter

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Abstract

As in many other scientific fields, data-driven methods are rapidly impacting multiscale modeling. This chapter will illustrate some of the many ways advanced statistical models and a data-centric perspective help augmenting computer simulations in soft matter. A specific focus on force fields, sampling, and simulation analysis is presented, taking advantage of machine learning, high-throughput schemes, and Bayesian inference.

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Acknowledgements

Various discussions have helped shape some of the views developed in this chapter. I am especially grateful to Denis Andrienko, Kurt Kremer, Joseph F. Rudzinski, Omar Valsson, and Anatole von Lilienfeld.

This work was supported in part by the Emmy Noether Programme of the Deutsche Forschungsgemeinschaft (DFG).

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Authors and Affiliations

  1. Theory Group, Max Planck Institute for Polymer Research, Mainz, Germany

    Tristan Bereau

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  1. Tristan Bereau
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Correspondence to Tristan Bereau .

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Editors and Affiliations

  1. Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Wanda Andreoni

  2. Department of Nuclear Science & Engineering , Massachusetts Institute of Technology , Cambridge, Massachusetts, USA

    Sidney Yip

Section Editor information

  1. MPI for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany

    Kurt Kremer

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Bereau, T. (2018). Data-Driven Methods in Multiscale Modeling of Soft Matter. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling . Springer, Cham. https://doi.org/10.1007/978-3-319-42913-7_40-1

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  • DOI: https://doi.org/10.1007/978-3-319-42913-7_40-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42913-7

  • Online ISBN: 978-3-319-42913-7

  • eBook Packages: Springer Reference Physics and AstronomyReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics

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