Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture.
In this work, we have developed quantitative structure-property relationship (QSPR) models using advanced machine learning algorithms that can rapidly and accurately recognize high-performing metal organic framework (MOF) materials for CO2 capture. More specifically, QSPR classifiers have been developed that can, in a fraction of a section, identify candidate MOFs with enhanced CO2 adsorption capacity (>1 mmol/g at 0.15 bar and >4 mmol/g at 1 bar). The models were tested on a large set of 292 050 MOFs that were not part of the training set. The QSPR classifier could recover 945 of the top 1000 MOFs in the test set while flagging only 10% of the whole library for compute intensive screening. Thus, using the machine learning classifiers as part of a high-throughput screening protocol would result in an order of magnitude reduction in compute time and allow intractably large structure libraries and search spaces to be screened.
Centre for Catalysis Research and Innovation, Department of Chemistry, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario K1N 6N5, Canada.
Michael
M
Fernandez
Centre for Catalysis Research and Innovation, Department of Chemistry, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario K1N 6N5, Canada.
Peter G
PG
Boyd
Centre for Catalysis Research and Innovation, Department of Chemistry, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario K1N 6N5, Canada.
Thomas D
TD
Daff
Centre for Catalysis Research and Innovation, Department of Chemistry, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario K1N 6N5, Canada.
Mohammad Zein
MZ
Aghaji
Centre for Catalysis Research and Innovation, Department of Chemistry, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario K1N 6N5, Canada.
Tom K
TK
Woo
MOFs; QSPR models; computer aided design; gas adsorption; nanoporous materials; virtual screening