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Thermodynamic rules for zeolite formation from machine learning based global optimization
While the [TO(4)] tetrahedron packing rule leads to millions of likely zeolite structures, there are currently only 252 types of zeolite frameworks reported after decades of synthetic efforts. The subtle synthetic conditions, e.g. the structure-directing agents, pH and the feed ratio, were often bla...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162439/ https://www.ncbi.nlm.nih.gov/pubmed/34094273 http://dx.doi.org/10.1039/d0sc03918g |
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author | Ma, Sicong Shang, Cheng Wang, Chuan-Ming Liu, Zhi-Pan |
author_facet | Ma, Sicong Shang, Cheng Wang, Chuan-Ming Liu, Zhi-Pan |
author_sort | Ma, Sicong |
collection | PubMed |
description | While the [TO(4)] tetrahedron packing rule leads to millions of likely zeolite structures, there are currently only 252 types of zeolite frameworks reported after decades of synthetic efforts. The subtle synthetic conditions, e.g. the structure-directing agents, pH and the feed ratio, were often blamed for the limited zeolite types due to the complex kinetics. Here by developing machine learning global optimization techniques, we are now able to establish the global potential energy surface of a typical zeolite system, Si(x)Al(y)P(z)O(2)H(y−z) with 12 T atoms (T: Si, Al and P) that is the general formula shared by CHA, ATS, ATO and ATV zeolite frameworks. After analyzing more than 10(6) minima data, we identify thermodynamic rules on energetics and local bonding patterns for stable zeolites. These rules provide general guidelines to classify zeolite types and correlate them with synthesis conditions. The machine learning based atomistic simulation thus paves a new way towards rational design and synthesis of stable zeolite frameworks with desirable compositions. |
format | Online Article Text |
id | pubmed-8162439 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-81624392021-06-04 Thermodynamic rules for zeolite formation from machine learning based global optimization Ma, Sicong Shang, Cheng Wang, Chuan-Ming Liu, Zhi-Pan Chem Sci Chemistry While the [TO(4)] tetrahedron packing rule leads to millions of likely zeolite structures, there are currently only 252 types of zeolite frameworks reported after decades of synthetic efforts. The subtle synthetic conditions, e.g. the structure-directing agents, pH and the feed ratio, were often blamed for the limited zeolite types due to the complex kinetics. Here by developing machine learning global optimization techniques, we are now able to establish the global potential energy surface of a typical zeolite system, Si(x)Al(y)P(z)O(2)H(y−z) with 12 T atoms (T: Si, Al and P) that is the general formula shared by CHA, ATS, ATO and ATV zeolite frameworks. After analyzing more than 10(6) minima data, we identify thermodynamic rules on energetics and local bonding patterns for stable zeolites. These rules provide general guidelines to classify zeolite types and correlate them with synthesis conditions. The machine learning based atomistic simulation thus paves a new way towards rational design and synthesis of stable zeolite frameworks with desirable compositions. The Royal Society of Chemistry 2020-09-02 /pmc/articles/PMC8162439/ /pubmed/34094273 http://dx.doi.org/10.1039/d0sc03918g Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/ |
spellingShingle | Chemistry Ma, Sicong Shang, Cheng Wang, Chuan-Ming Liu, Zhi-Pan Thermodynamic rules for zeolite formation from machine learning based global optimization |
title | Thermodynamic rules for zeolite formation from machine learning based global optimization |
title_full | Thermodynamic rules for zeolite formation from machine learning based global optimization |
title_fullStr | Thermodynamic rules for zeolite formation from machine learning based global optimization |
title_full_unstemmed | Thermodynamic rules for zeolite formation from machine learning based global optimization |
title_short | Thermodynamic rules for zeolite formation from machine learning based global optimization |
title_sort | thermodynamic rules for zeolite formation from machine learning based global optimization |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162439/ https://www.ncbi.nlm.nih.gov/pubmed/34094273 http://dx.doi.org/10.1039/d0sc03918g |
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