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From predictive modelling to machine learning and reverse engineering of colloidal self-assembly

Abstract

An overwhelming diversity of colloidal building blocks with distinct sizes, materials and tunable interaction potentials are now available for colloidal self-assembly. The application space for materials composed of these building blocks is vast. To make progress in the rational design of new self-assembled materials, it is desirable to guide the experimental synthesis efforts by computational modelling. Here, we discuss computer simulation methods and strategies used for the design of soft materials created through bottom-up self-assembly of colloids and nanoparticles. We describe simulation techniques for investigating the self-assembly behaviour of colloidal suspensions, including crystal structure prediction methods, phase diagram calculations and enhanced sampling techniques, as well as their limitations. We also discuss the recent surge of interest in machine learning and reverse-engineering methods. Although their implementation in the colloidal realm is still in its infancy, we anticipate that these data-science tools offer new paradigms in understanding, predicting and (inverse) design of novel colloidal materials.

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Fig. 1: Effective interactions between colloids and nanoparticles, with typical examples of the resulting self-assembly behaviour observed in experiments and predicted in simulations.
Fig. 2: Example applications of correlation functions and bond order parameters to classify thermodynamic phases and pathways.
Fig. 3: Examples of ML techniques applied to atomic and colloidal systems.

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Acknowledgements

We thank Z. Wang for designing the figures. E.L. is supported by the Center for Bio-Inspired Energy Science (CBES), an Energy Frontier Research Center funded by the US Department of Energy, Office of Science, Basic Energy Sciences BES, under award no. DE-SC0000989.

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Dijkstra, M., Luijten, E. From predictive modelling to machine learning and reverse engineering of colloidal self-assembly. Nat. Mater. 20, 762–773 (2021). https://doi.org/10.1038/s41563-021-01014-2

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