NRI: INT: Self-Assembly of Modular Robots Constructed using DNA: Modeling and Manufacturing Nanostructures with Graph Neural Networks and DNA Origami

Future micro-scale robotic devices will be integrated in broad aspects of human life, having explicit impact on applications as diverse as non-invasive surgical procedures to advanced electronics manufacturing. However, at very small physical scales it is essentially impossible to manipulate, in a conventional sense, the physical components that are necessary to construct mechanical structures.

In addition, it is also very difficult to see or otherwise sense with fidelity mechanisms that are formed at extremely small scales, e.g., the nano-scale. This inability to easily sense nano-scale mechanisms makes it difficult to gather data on the efficacy of different assembly processes or collect feedback data that would be required to control active mechanisms, i.e., to actuate them to induce some desired motion.

Therefore, to address these difficulties, this work proposes to develop a synergistic framework that combines ideas related to contemporary top-down and bottom-up manufacturing processes with those from the machine learning (ML), artificial intelligence (AI), and robotics communities to address what we see as the largest current barriers to the practical deployment of future nano-scale robotic systems: 1) manipulating components for assembly, 2) the availability of low-cost, readily available sensing, and 3) actuating the mechanisms once formed. In addition, we propose to make broader contributions to the research community by establishing a framework for archiving multimodal data about nanostructure formation statistics that will be shared with and ideally added to by other researchers.

Lastly, from an educational perspective, the PIs have already begun to educate middle school students about artificial intelligence and DNA nanotechnology and intend we further these efforts by introducing a novel macroscale model that can serve as an interactive activity for teaching K-12 students more about the confluence of DNA nanotechnology and artificial intelligence.

This work develops a novel framework that optimizes the outcome of physical processes wherein modular nanoscale robots self-assemble from a set of nano submodules. The main contribution of the framework to be developed is to reduce the uncertainty in large-scale self-assembly processes wherein the objective is to create nanoscale superstructures with specific designs.

We propose to use DNA origami to create modular components in a nanoscale test bed because DNA origami is an excellent tool for forming different geometric constructs, e.g., a honeycomb-like truss, with subnanometer precision. We intend to use a graph neural network framework to model complex, large-scale self-assembly processes as distributions over a discrete space of modular DNA superstructures that are represented using graphs.

We hypothesize that this approach will allow us to optimize the process conditions during the manufacturing trials, such as the number of unique connections between components, thereby maximizing the yield of desired superstructures. Our key contributions include 1) learn to map low dimensional characterization data to a graph-based representation of the corresponding superstructure populations; 2) generate probabilistic graphs that represent the distribution of superstructures formed for an arbitrary set of manufacturing conditions; and, 3) apply optimization techniques to our generative model to find the optimal manufacturing conditions for maximizing the yield of a desired superstructure.

In addition, Taylor and Travers plan to leverage their ongoing collaboration that focuses on using global stimuli like magnetic actuation to induce motion in systems constructed using magnetic micro- and nanoparticles to perform preliminary motion studies that will be conducted using chemical and magnetic field actuation.

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.