Collaborative Research: DMREF: Designer 3D Mesoscale Materials Synthesized in the Self-Assembly Foundry

Self-assembly is one of the most promising avenues for the manufacturing/synthesis of materials and systems with exquisite control over nanoscale features while being fast, scalable, and inexpensive. It could enable the next revolution in integrated systems and designer mesoscale materials for multiple applications including information systems, sensing, actuation, and artificial intelligence.

However, there are still many challenges in utilizing self-assembly as a precision fabrication technique. This project will develop a self-assembly foundry by implementing a dual assembly line in which experimental, molecular simulations, and artificial intelligence techniques are deployed simultaneously to create designer 3-dimensional (3D) nanostructured systems.

This effort will examine the limits of 3D self-assembly to accelerate the fabrication of custom systems, from interconnected nanosystems used in computer chips to designer mechanical nanostructures for sensing and actuation. Particular emphasis will be placed in developing new manufacturing routes using topological principles. The broader impacts of this project envision a basis for training a new generation of scientist or engineers that can engage effectively with industry and academia.

This project will also lead to the training of community college students and the development of online learning materials, as well as public engagement activities. The convergence of academia and industry; theory, computation, and experiment; different mentoring perspectives; and the high-level view of the self-assembly manufacturing process will provide a rich environment for the participants to develop new knowledge, skills, and abilities, with a strong emphasis on training and knowledge transfer.

The scientific challenge that this project will tackle is that of being able to create arbitrary 3D structures using block copolymers, which are polymers composed of two or more chemistries. Using state-of-the-art computational techniques in conjunction with experiments, this project aims to unravel the design rules for self-assembling layer-by-layer systems that have a predesigned, non-symmetric, and intricate 3D structure from the information contained in the substrate.

While in the optics field this corresponds to creating a hologram, the rules for doing so in a self-assembly system are not clear. Fault tolerance is one of the main challenges and topological constructs will be sought to make the self-assembly process robust. Direct learning through a new dual self-assembly line concept that combines, on one branch the physical process and in another branch the virtual process, will also be employed.

Both of these assembly lines will be connected through an artificial intelligence engine to find the hidden correlations and learn the design rules. Thus, this project has the potential to set a blueprint for the future of nanomanufacturing.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.