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Machine learning-based optimal control for colloidal self-assembly
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DOI：10.1002/aic.70389.png)
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Abstract
En 中文
Achieving precise control of colloidal self-assembly into specific patterns remains a longstanding challenge due to the complex process dynamics. Recently, machine learning-based state representation and reinforcement learning-based control strategies have started to accumulate popularity in the field, showing great potential as an automatable and generalizable approach to producing patterned colloidal assembly. In this work, we proposed a machine learning-based optimal control framework, combining unsupervised learning and graph convolutional neural network for state representation with deep reinforcement learning-based optimal control policy calculation, to provide a data-driven control strategy that can potentially be generalized to other many-body self-assembly systems. With Brownian Dynamics simulations, we demonstrated its superior performance as compared to traditional order parameter-based state description, and its efficacy in obtaining ordered two-dimensional spherical colloidal self-assembly in an electric field-mediated system with an actual success rate of 97%.
Keywords:
colloidal self-assembly
deep Q-learning
graph convolutional neural network
machine learning
optimal control
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4
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