Machine learning accelerated high-throughput screening of zeolites for the selective adsorption of xylene isomers.

ABSTRACT: The production of widely used polymers such as polyester currently relies upon the chemical separation of and transformation of xylene isomers. The least valuable but most prevalent isomer is meta-xylene which can be selectively transformed into the more useful and expensive para-xylene isomer using a zeolite catalyst but at a high energy cost. In this work, high-throughput screening of existing and hypothetical zeolite databases containing more than two million structures was performed, using a combination of classical simulation and deep neural network methods to identify promising materials for selective adsorption of meta-xylene. Novel anomaly detection techniques were applied to the heavily biased classification task of identifying structures with a selectivity greater than that of the best performing existing zeolite, ZSM-5 (MFI topology). Eight hypothetical zeolite topologies are found to be several orders of magnitude more selective towards meta-xylene than ZSM-5 which may provide an impetus for synthetic efforts to realise these promising materials. Moreover, the leading hypothetical frameworks identified from the screening procedure require a markedly lower operating temperature to achieve the diffusion seen in existing materials, suggesting significant energetic savings if the frameworks can be realised.

SUBMITTER: Hewitt D 

PROVIDER: S-EPMC9667951 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature