Intelligent Monte Carlo: A new paradigm for inverse polymerization engineering
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Traditional computational methods simulate the microstructure of polymer chains from input reaction conditions, but a need exists for predicting optimum reaction conditions in a computationally-demanding multi-variable space leading to the synthesis of predesigned microstructures and architectures. We introduce herein the Intelligent Monte Carlo (IMC) approach, able to predict optimum reaction conditions for synthesizing copolymers with predefined, complex microstructures as input. This is rendered possible by a combination of Kinetic Monte Carlo (KMC) simulation with Artificial Intelligence concepts, which enables a reasonably enhanced convergence to optimum reactions conditions. Chain shuttling polymerization was chosen as a first test case due to its complexity and the intricate multi-block microstructures that are formed; whose tailoring requires multiple parameters. The IMC approach located optimum reaction conditions for the synthesis of olefinic multi-block copolymers with specific microstructures. This approach provides a new platform for identifying complex reaction conditions to ‘produce’ and ‘tailor-make’ materials with precisely predefined microstructures and facilitates the development of meaningful structure-property relationships.
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Yousef Mohammadi, Mohammad Reza Saeb, Alexander Penlidis, Esmaiel Jabbari, Philippe Zinck, Florian Stadler, Krzysztof Matyjaszewski (2018). Intelligent Monte Carlo: A new paradigm for inverse polymerization engineering. UWSpace. http://hdl.handle.net/10012/16306