Hewitt, Daniel;
Pope, Tom;
Sarwar, Misbah;
Turrina, Alessandro;
Slater, Ben;
(2022)
Machine learning accelerated high-throughput screening of zeolites for the selective adsorption of xylene isomers.
Chemical Science
10.1039/d2sc03351h.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Machine learning accelerated high-throughput screening of zeolites for the selective adsorption of xylene isomers |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1039/d2sc03351h |
| Publisher version: | https://doi.org/10.1039/d2sc03351h |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Physical Sciences, Chemistry, Multidisciplinary, Chemistry, UNITED-ATOM DESCRIPTION, TRANSFERABLE POTENTIALS, PHASE-EQUILIBRIA, MONTE-CARLO, ISOMERIZATION, FRAMEWORKS |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Chemistry |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159653 |
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