A Neural Network-based Optimal Control Framework for Colloidal Self-Assembly

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Controlling colloidal self-assembly for ordered structures is a promising route to novel physicochemical properties, which stands to benefit applications in photonics, biomaterials, pharmaceutics, energy harvest, and advanced communication. However, high dimensionality and complex dynamics are barriers to rapid production of ordered structures. To overcome these barriers, accurate and generalizable state representation and a reliable and computationally efficient approach to describe and predict the system dynamics are needed. This project proposes a novel neural network-based optimal control framework, drawing on multidisciplinary expertise from colloidal systems, machine learning, and optimal control theory. The success of the work will: 1) contribute to a potentially automatable optimal control framework for rapid production of user-defined assembly structures, 2) benefit studies on related atomic and molecular self-assembly systems, such as crystallization for drug production and nuclear waste handling, as well as protein self-assembly; and 3) promote interdisciplinary research on combining advanced machine learning techniques and control theory to tackle complex problems that are challenging to address with traditional domain approaches. Throughout this project, the PI will educate and mentor students from middle school to the master’s level on molecular self-assembly, advanced control theory and machine learning topics. Focusing on an electric field-mediated colloidal self-assembly process, the researcher proposes a neural network-optimal control integrated approach, to tackle the long-standing challenges associated with controlling a stochastic and high dimensional small particle self-assembly process. A convolutional neural network will be used to represent and classify the system state, avoiding the extensive trial-and-error exploration and validation, and the limited transferability associated with order parameters. The project will also deploy a stochastic neural network, to be trained with time series of the assembly process, to capture and predict the system dynamics. Combining the convolutional and stochastic neural network with reinforcement learning, an optimal control policy will then be computed to guide the manipulation of the voltage level of the external electric field, to rapidly drive the assembly to the desired structure. The proposed work presents an innovative solution to state representation and classification, and an efficient system dynamics simulation of the stochastic, high dimensional colloidal system. Due to the data-driven nature of machine learning models, the proposed framework is transferable to systems with different internal interactions due to different driving forces and/or different particle sizes and shapes.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.