Machine learning accelerated high-throughput screening of zeolites for the selective adsorption of xylene isomers-Reference-Cited by-同舟云学术

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

Published:2022 Issue:44 Volume:13 Page:13178-13186
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Hewitt Daniel¹^ORCID,Pope Tom¹^ORCID,Sarwar Misbah²^ORCID,Turrina Alessandro³^ORCID,Slater Ben¹^ORCID

Affiliation:

1. Department of Chemistry, University College London, 20 Gordon Street, London, WC1E 6BT, UK

2. Johnson Matthey Technology Centre, Sonning Common, Reading, RG4 9NH, UK

3. Johnson Matthey Technology Centre, Chilton, P.O. Box 1, Belasis Avenue, Billingham, TS23 1LB, UK

Abstract

A combination of machine learning and high throughput simulation has identified several potential zeolite structures that appear to outperform the leading commercially used material and explained the key factors for high selectivity.

Funder

Engineering and Physical Sciences Research Council

Royal Society

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SC/D2SC03351H

Reference39 articles.

1. Seven chemical separations to change the world

2. Isomerization and disproportionation of m-xylene

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4. Hierarchical ZSM-5 Zeolites in Shape-Selective Xylene Isomerization: Role of Mesoporosity and Acid Site Speciation

5. Metal–Organic Frameworks for Xylene Separation: From Computational Screening to Machine Learning

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