Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization
Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymers with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to achieve the targeted polymer property. Th...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Polymers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4360/16/7/945 |
| _version_ | 1797212075901059072 |
|---|---|
| author | Jelena Fiosina Philipp Sievers Gavaskar Kanagaraj Marco Drache Sabine Beuermann |
| author_facet | Jelena Fiosina Philipp Sievers Gavaskar Kanagaraj Marco Drache Sabine Beuermann |
| author_sort | Jelena Fiosina |
| collection | DOAJ |
| description | Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymers with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to achieve the targeted polymer property. The objectives for optimization include the maximal similarity of molar mass distributions (MMDs) compared to the target MMDs, a minimal reaction time, and maximal monomer conversion. The method is tested for vinyl acetate radical polymerizations and can be adopted to other monomers. The data for the optimization procedure are generated by an in-house-developed kinetic Monte-Carlo (kMC) simulator for a selected recipe search space. The proposed reverse engineering algorithm comprises several steps: kMC simulations for the selected recipe search space to derive initial data, performing MOO for a targeted MMD, and the identification of the Pareto optimal space. The last step uses a weighted sum optimization function to calculate the weighted score of each candidate polymerization condition. To decrease the execution time, clustering of the search space based on MMDs is applied. The performance of the proposed approach is tested for various target MMDs. The suggested MOO-based reverse engineering provides multiple recipe candidates depending on competing objectives. |
| first_indexed | 2024-04-24T10:36:37Z |
| format | Article |
| id | doaj.art-44042a2de9ea4febafbcb040e1d1705f |
| institution | Directory Open Access Journal |
| issn | 2073-4360 |
| language | English |
| last_indexed | 2024-04-24T10:36:37Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Polymers |
| spelling | doaj.art-44042a2de9ea4febafbcb040e1d1705f2024-04-12T13:25:10ZengMDPI AGPolymers2073-43602024-03-0116794510.3390/polym16070945Reverse Engineering of Radical Polymerizations by Multi-Objective OptimizationJelena Fiosina0Philipp Sievers1Gavaskar Kanagaraj2Marco Drache3Sabine Beuermann4Institute of Informatics, Clausthal University of Technology, Julius-Albert-Str. 4, 38678 Clausthal-Zellerfeld, GermanyInstitute of Technical Chemistry, Clausthal University of Technology, Arnold-Sommerfeld-Strasse 4, 38678 Clausthal-Zellerfeld, GermanyInstitute of Informatics, Clausthal University of Technology, Julius-Albert-Str. 4, 38678 Clausthal-Zellerfeld, GermanyInstitute of Technical Chemistry, Clausthal University of Technology, Arnold-Sommerfeld-Strasse 4, 38678 Clausthal-Zellerfeld, GermanyInstitute of Technical Chemistry, Clausthal University of Technology, Arnold-Sommerfeld-Strasse 4, 38678 Clausthal-Zellerfeld, GermanyReverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymers with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to achieve the targeted polymer property. The objectives for optimization include the maximal similarity of molar mass distributions (MMDs) compared to the target MMDs, a minimal reaction time, and maximal monomer conversion. The method is tested for vinyl acetate radical polymerizations and can be adopted to other monomers. The data for the optimization procedure are generated by an in-house-developed kinetic Monte-Carlo (kMC) simulator for a selected recipe search space. The proposed reverse engineering algorithm comprises several steps: kMC simulations for the selected recipe search space to derive initial data, performing MOO for a targeted MMD, and the identification of the Pareto optimal space. The last step uses a weighted sum optimization function to calculate the weighted score of each candidate polymerization condition. To decrease the execution time, clustering of the search space based on MMDs is applied. The performance of the proposed approach is tested for various target MMDs. The suggested MOO-based reverse engineering provides multiple recipe candidates depending on competing objectives.https://www.mdpi.com/2073-4360/16/7/945polymerization reverse engineeringclusteringmulti-objective optimization |
| spellingShingle | Jelena Fiosina Philipp Sievers Gavaskar Kanagaraj Marco Drache Sabine Beuermann Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization Polymers polymerization reverse engineering clustering multi-objective optimization |
| title | Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization |
| title_full | Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization |
| title_fullStr | Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization |
| title_full_unstemmed | Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization |
| title_short | Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization |
| title_sort | reverse engineering of radical polymerizations by multi objective optimization |
| topic | polymerization reverse engineering clustering multi-objective optimization |
| url | https://www.mdpi.com/2073-4360/16/7/945 |
| work_keys_str_mv | AT jelenafiosina reverseengineeringofradicalpolymerizationsbymultiobjectiveoptimization AT philippsievers reverseengineeringofradicalpolymerizationsbymultiobjectiveoptimization AT gavaskarkanagaraj reverseengineeringofradicalpolymerizationsbymultiobjectiveoptimization AT marcodrache reverseengineeringofradicalpolymerizationsbymultiobjectiveoptimization AT sabinebeuermann reverseengineeringofradicalpolymerizationsbymultiobjectiveoptimization |