Literature DB >> 22915542

Similarity-driven discovery of zeolite materials for adsorption-based separations.

Richard L Martin1, Thomas F Willems, Li-Chiang Lin, Jihan Kim, Joseph A Swisher, Berend Smit, Maciej Haranczyk.   

Abstract

Crystalline porous materials can be exploited in many applications. Discovery of materials with optimum adsorption properties typically involves expensive brute-force characterization of large sets of materials. An alternative approach based on similarity searching that enables discovery of materials with optimum adsorption for CO(2) and other molecules at a fraction of the cost of brute-force characterization is demonstrated.
Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Year:  2012        PMID: 22915542     DOI: 10.1002/cphc.201200554

Source DB:  PubMed          Journal:  Chemphyschem        ISSN: 1439-4235            Impact factor:   3.102


  5 in total

1.  Chemical similarity of molecules with physiological response.

Authors:  Izudin Redžepović; Boris Furtula
Journal:  Mol Divers       Date:  2022-08-17       Impact factor: 3.364

2.  Automatic framework for extraction and characterization of wetting front propagation using tomographic image sequences of water infiltrated soils.

Authors:  Dionicio Vasquez; Jacob Scharcanski; Alexander Wong
Journal:  PLoS One       Date:  2015-01-20       Impact factor: 3.240

3.  Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage.

Authors:  Aaron W Thornton; Cory M Simon; Jihan Kim; Ohmin Kwon; Kathryn S Deeg; Kristina Konstas; Steven J Pas; Matthew R Hill; David A Winkler; Maciej Haranczyk; Berend Smit
Journal:  Chem Mater       Date:  2017-03-08       Impact factor: 9.811

4.  High-Throughput Screening Approach for Nanoporous Materials Genome Using Topological Data Analysis: Application to Zeolites.

Authors:  Yongjin Lee; Senja D Barthel; Paweł Dłotko; Seyed Mohamad Moosavi; Kathryn Hess; Berend Smit
Journal:  J Chem Theory Comput       Date:  2018-07-30       Impact factor: 6.006

5.  Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks.

Authors:  Aditi S Krishnapriyan; Joseph Montoya; Maciej Haranczyk; Jens Hummelshøj; Dmitriy Morozov
Journal:  Sci Rep       Date:  2021-04-26       Impact factor: 4.996

  5 in total

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