Integrative Approaches for DNA Sequence‐Controlled Functional Materials

Abstract DNA carries a symbolic code that can be used to program the functional properties of DNA‐based materials. Effective programming, however, requires the ability to predict how DNA sequence alterations modify emergent material properties, occurring at lengthscales a thousand to a million times larger than the base‐pair level. This multiscale challenge can be addressed with integrative approaches that combine simulations, experiments and machine learning. This perspective article discusses how integrative approaches enable the characterization and design of functional materials composed of networks of DNA nanostructures with DNA‐sequence‐encoded interaction sites. Programmed at the base‐pair level, these DNA nanomotifs impart complex sequence‐property relationships by assembling into higher‐order networks. State‐of‐the‐art experimental and computational methods are presented for studying DNA nanomotif materials across scales. Furthermore, the potential of data‐driven machine learning for transferring information between simulation scales is demonstrated, and its integration into iterative design strategies is discussed. Using DNA as a programmable building block, potential future applications are outlined in the form of biomimetic systems, mechanically tunable biomaterials, and information processing platforms.