High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning mod...
High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model
About this item
Full title
Author / Creator
Xuefeng Bai , Yi Li , Yabo Xie , Qiancheng Chen , Xin Zhang and Jian-Rong Li
Publisher
KeAi Communications Co., Ltd
Journal title
Language
English
Formats
Publication information
Publisher
KeAi Communications Co., Ltd
Subjects
More information
Scope and Contents
Contents
The high porosity and tunable chemical functionality of metal-organic frameworks (MOFs) make it a promising catalyst design platform. High-throughput screening of catalytic performance is feasible since the large MOF structure database is available. In this study, we report a machine learning model for high-throughput screening of MOF catalysts for...
Alternative Titles
Full title
High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_38fabfddce154176aa7403f8893bb44a
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_38fabfddce154176aa7403f8893bb44a
Other Identifiers
E-ISSN
2468-0257
