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Article dans une revue

Intelligent Machine Learning: Tailor-Making Macromolecules

Abstract : Nowadays, polymer reaction engineers seek robust and effective tools to synthesizecomplex macromolecules with well-defined and desirable microstructural and architecturalcharacteristics. Over the past few decades, several promising approaches, such as controlled living(co)polymerization systems and chain-shuttling reactions have been proposed and widely applied tosynthesize rather complex macromolecules with controlled monomer sequences. Despite the uniquepotential of the newly developed techniques, tailor-making the microstructure of macromolecules bysuggesting the most appropriate polymerization recipe still remains a very challenging task. In thecurrent work, two versatile and powerful tools capable of effectively addressing the aforementionedquestions have been proposed and successfully put into practice. The two tools are establishedthrough the amalgamation of the Kinetic Monte Carlo simulation approach and machine learningtechniques. The former, an intelligent modeling tool, is able to model and visualize the intricateinter-relationships of polymerization recipes/conditions (as input variables) and microstructuralfeatures of the produced macromolecules (as responses). The latter is capable of precisely predictingoptimal copolymerization conditions to simultaneously satisfy all predefined microstructural features.The effectiveness of the proposed intelligent modeling and optimization techniques for solving thisextremely important ‘inverse’ engineering problem was successfully examined by investigatingthe possibility of tailor-making the microstructure of Olefin Block Copolymers via chain-shuttlingcoordination polymerization.
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https://hal.univ-lille.fr/hal-03038672
Contributeur : Lilloa Université de Lille <>
Soumis le : jeudi 3 décembre 2020 - 15:50:08
Dernière modification le : mercredi 17 février 2021 - 09:56:08
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Yousef Mohammadi, Mohammad Reza Saeb, Alexander Penlidis, Esmaiel Jabbari, Florian J. Stadler, et al.. Intelligent Machine Learning: Tailor-Making Macromolecules. Polymers, MDPI, 2019, Polymers, 11 (4), pp.579-592. ⟨10.3390/polym11040579⟩. ⟨hal-03038672⟩

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