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Data-Driven Methods in Multiscale Modeling of Soft Matter

  • Tristan BereauEmail author
Reference work entry

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.

Notes

Acknowledgments

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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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Theory GroupMax Planck Institute for Polymer ResearchMainzGermany

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