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Abstract The ability to control forces between sub-micron-scale building blocks offers significant potential for designing new materials through self-assembly. Traditionally, this involves identifying a crystal structure with a desired property and then designing building-block interactions so that it assembles spontaneously. However, this paradigm fails for structurally disordered solids, which lack a well-defined structure. Here, we show that disordered solids can still be treated from an inverse self-assembly perspective by bypassing structure and directly targeting material properties. Using the Poisson’s ratio as a primary example, we demonstrate how differentiable programming links interaction parameters with emergent behavior, enabling iterative training to achieve the desired Poisson’s ratio. We also tune other properties, including pressure and local 8-fold structural order, and can even control multiple properties simultaneously. This robust, transferable, and scalable approach can handle a wide variety of systems and properties, demonstrating the utility of disordered solids as a practical avenue for self-assembly platforms.

Abstract The self-assembly of complex structures from a set of non-identical building blocks is a hallmark of soft matter and biological systems, including protein complexes, colloidal clusters, and DNA-based assemblies. Predicting the dependence of the equilibrium assembly yield on the concentrations and interaction energies of building blocks is highly challenging, owing to the difficulty of computing the entropic contributions to the free energy of the many structures that compete with the ground state configuration. While these calculations yield well known results for spherically symmetric building blocks, they do not hold when the building blocks have internal rotational degrees of freedom. Here we present an approach for solving this problem that works with arbitrary building blocks, including proteins with known structure and complex colloidal building blocks. Our algorithm combines classical statistical mechanics with recently developed computational tools for automatic differentiation. Automatic differentiation allows efficient evaluation of equilibrium averages over configurations that would otherwise be intractable. We demonstrate the validity of our framework by comparison to molecular dynamics simulations of simple examples, and apply it to calculate the yield curves for known protein complexes and for the assembly of colloidal shells.

Gels filtering particles by interactions are a goal of nanotechnology; this is difficult when particles are larger than the mesh of the gel. Here, the authors present an equilibrium mechanism where binding dynamics of crosslinks are affected by interacting particles so that particles experience enhanced diffusion.