Multiscale Modeling for the Design of Deoxyribonucleic Acid-Functionalized Nanoparticles for Targeted Self-Assembly...

Self-assembly of ordered structures from suitably designed building blocks is a promising approach for the generation of soft materials with optimized properties for target applications. However, it is still an open question how to generally design realizable building blocks that lead to the desired phases. In this work, DNA-functionalized nanoparticles are used as model building blocks, and the bicontinuous double gyroid is chosen as the target structure. An effective multiscale search strategy is implemented to explore a large building-block design space, where free energy calculations are first used to coarse-grain our originally fine-grained model of the building blocks and then quickly evaluate the fitness of each design. These data are then fed into a machine learning algorithm that allows obtaining predictions for all candidates in our design space through an active learning loop. Successful coarse-grained designs are identified and validated through interfacial pinning calculations with the fine-grained model. This framework leads to the development of specific, experimentally relevant designs of DNA-functionalized nanoparticles that self-assemble into the target phase. The advocated methodology can be extended to other types of building blocks and target structures.