Intelligent Machine Learning: Tailor-Making Macromolecules - Université de Lille Accéder directement au contenu
Article Dans Une Revue Polymers Année : 2019

Intelligent Machine Learning: Tailor-Making Macromolecules

Résumé

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.
Fichier principal
Vignette du fichier
polymers-11-00579.pdf (3.28 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03038672 , version 1 (03-12-2020)

Licence

Identifiants

Citer

Yousef Mohammadi, Mohammad Reza Saeb, Alexander Penlidis, Esmaiel Jabbari, Florian J. Stadler, et al.. Intelligent Machine Learning: Tailor-Making Macromolecules. Polymers, 2019, Polymers, 11 (4), pp.579-592. ⟨10.3390/polym11040579⟩. ⟨hal-03038672⟩
26 Consultations
45 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More