Rapid and accurate machine learning recognition of high performing metal organic frameworks for CO2 capture

Journal of Physical Chemistry Letters

Volumn 5, Issue 17, 2014, Pages 3056-3060

  Fernandez, Michael ^a   Boyd, Peter G ^a   Daff, Thomas D ^a   Aghaji, Mohammad Zein ^a   Woo, Tom K ^a  

^a UNIVERSITY OF OTTAWA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CARBON DIOXIDE; CLASSIFICATION (OF INFORMATION); LEARNING ALGORITHMS; METAL-ORGANIC FRAMEWORKS; ORGANIC POLYMERS; ORGANOMETALLICS; POROUS MATERIALS;

CO2 ADSORPTION; HIGH THROUGHPUT SCREENING; LARGE STRUCTURES; MAGNITUDE REDUCTION; METAL ORGANIC FRAMEWORK MATERIALS; QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIP MODELS; SEARCH SPACES; TRAINING SETS;

MACHINE LEARNING;

EID: 84923174665     PISSN: None     EISSN: 19487185     Source Type: Journal    
DOI: 10.1021/jz501331m     Document Type: Article

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