A computational toolbox for heterogeneous self-assembly

Self-assembly is a process in which small building blocks spontaneously form multimeric structures. This phenomenon is one of the hallmarks of soft matter, where it appears at all scales, from protein complexes, to lipid vesicles, to colloidal clusters. One of the hardest challenges in soft matter self-assembly is predicting the yield of a given structure from the properties of its building blocks and the conditions of the surrounding environment. In general, the parameter space is too large to be explored through experiments, which are often time-intensive and expensive. A more practical solution is to use analytical theories and simulations, which can improve our ability to engineer new materials with carefully designed properties. Previous efforts in this space have focused on the theoretical modeling of building blocks with simple geometry, such as spherical colloids. However, a theory for the self-assembly of building blocks with less trivial shapes, typical of, e.g. protein molecules, is still lacking. Here I will present an analytical/computational toolbox to compute the yield of structures whose building blocks can have arbitrary shape and interactions.