Rational design of patchy colloids landscape engineering

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. A new approach for inverse design of self-assembling building blocks by rational sculpting of the underlying self-assembly free energy landscape.