Machine Learning and Informatics Based Elucidation of Reaction Pathways for Upcycling Model Polyolefin to Aromatics.

ABSTRACT: Catalytic upcycling of plastics results in a complex network of potentially thousands of reactions and intermediates. Manual analysis of such a network using ab initio methods to identify plausible reaction pathways and rate-controlling steps is intractable. Here, we combine informatics-based reaction network generation and machine learning based thermochemistry calculation to identify plausible (nonelementary step) pathways involved in dehydroaromatization of a model polyolefin, n-decane, to form aromatic products. All 78 aromatic molecules found involve a sequence comprising dehydrogenation, β-scission, and cyclization steps (in slightly different order). The plausible flux-carrying pathway depends on the family of reactions that is rate-controlling while the thermodynamic bottlen

SUBMITTER: Chang CF 

PROVIDER: S-EPMC10249406 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature