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Rational design of patchy colloids via landscape engineering

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Abstract

We present a new data-driven inverse design platform for self-assembling materials that we term “landscape engineering”. The essence of the approach is to sculpt the self-assembly free energy landscape to favor the formation of target aggregates by rational manipulation of building block properties. The approach integrates nonlinear manifold learning with hybrid Monte Carlo techniques to efficiently recover self-assembly landscapes, which we subsequently optimize using the covariance matrix adaptation evolutionary strategy (CMA-ES). We demonstrate the effectiveness of this technique in the design of anisotropic patchy colloids to form hollow polyhedral capsids. In the case of icosahedral capsids, our approach discovers a building block possessing a 76% improvement in the assembly rate over an initial expert-designed building block. In the case of octahedral clusters, our platform produces a building block with a 60% yield despite being challenged with a poor initial building block design incapable of forming stable octahedra.

Graphical abstract: Rational design of patchy colloids via landscape engineering

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Publication details

The article was received on 19 Aug 2017, accepted on 13 Oct 2017 and first published on 17 Oct 2017


Article type: Paper
DOI: 10.1039/C7ME00077D
Citation: Mol. Syst. Des. Eng., 2017, Advance Article
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    Rational design of patchy colloids via landscape engineering

    A. W. Long and A. L. Ferguson, Mol. Syst. Des. Eng., 2017, Advance Article , DOI: 10.1039/C7ME00077D

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