Literature DB >> 24777001

High-throughput computational screening of metal-organic frameworks.

Yamil J Colón1, Randall Q Snurr.   

Abstract

There is an almost unlimited number of metal-organic frameworks (MOFs). This creates exciting opportunities but also poses a problem: how do we quickly find the best MOFs for a given application? Molecular simulations have advanced sufficiently that many MOF properties - especially structural and gas adsorption properties - can be predicted computationally, and molecular modeling techniques are now used increasingly to guide the synthesis of new MOFs. With increasing computational power and improved simulation algorithms, it has become possible to conduct high-throughput computational screening to identify promising MOF structures and uncover structure-property relations. We review these efforts and discuss future directions in this new field.

Entities:  

Year:  2014        PMID: 24777001     DOI: 10.1039/c4cs00070f

Source DB:  PubMed          Journal:  Chem Soc Rev        ISSN: 0306-0012            Impact factor:   54.564


  27 in total

1.  The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation.

Authors:  Arni Sturluson; Melanie T Huynh; Alec R Kaija; Caleb Laird; Sunghyun Yoon; Feier Hou; Zhenxing Feng; Christopher E Wilmer; Yamil J Colón; Yongchul G Chung; Daniel W Siderius; Cory M Simon
Journal:  Mol Simul       Date:  2019       Impact factor: 2.178

Review 2.  From computational high-throughput screenings to the lab: taking metal-organic frameworks out of the computer.

Authors:  Aurelia Li; Rocio Bueno-Perez; David Madden; David Fairen-Jimenez
Journal:  Chem Sci       Date:  2022-06-16       Impact factor: 9.969

3.  Machine-learning-assisted materials discovery using failed experiments.

Authors:  Paul Raccuglia; Katherine C Elbert; Philip D F Adler; Casey Falk; Malia B Wenny; Aurelio Mollo; Matthias Zeller; Sorelle A Friedler; Joshua Schrier; Alexander J Norquist
Journal:  Nature       Date:  2016-05-05       Impact factor: 49.962

4.  In silico prediction and screening of modular crystal structures via a high-throughput genomic approach.

Authors:  Yi Li; Xu Li; Jiancong Liu; Fangzheng Duan; Jihong Yu
Journal:  Nat Commun       Date:  2015-09-23       Impact factor: 14.919

5.  High-Throughput Computational Screening of the Metal Organic Framework Database for CH4/H2 Separations.

Authors:  Cigdem Altintas; Ilknur Erucar; Seda Keskin
Journal:  ACS Appl Mater Interfaces       Date:  2018-01-18       Impact factor: 9.229

6.  Methane Adsorption in Zr-Based MOFs: Comparison and Critical Evaluation of Force Fields.

Authors:  Steven Vandenbrande; Toon Verstraelen; Juan José Gutiérrez-Sevillano; Michel Waroquier; Veronique Van Speybroeck
Journal:  J Phys Chem C Nanomater Interfaces       Date:  2017-10-24       Impact factor: 4.126

7.  Effects of Force Field Selection on the Computational Ranking of MOFs for CO2 Separations.

Authors:  Derya Dokur; Seda Keskin
Journal:  Ind Eng Chem Res       Date:  2018-01-18       Impact factor: 3.720

8.  Computational Screening of MOFs for Acetylene Separation.

Authors:  Ayda Nemati Vesali Azar; Seda Keskin
Journal:  Front Chem       Date:  2018-02-27       Impact factor: 5.221

9.  Seed-mediated growth of MOF-encapsulated Pd@Ag core-shell nanoparticles: toward advanced room temperature nanocatalysts.

Authors:  Liyu Chen; Binbin Huang; Xuan Qiu; Xi Wang; Rafael Luque; Yingwei Li
Journal:  Chem Sci       Date:  2015-09-23       Impact factor: 9.825

10.  Molecular modeling of zinc paddlewheel molecular complexes and the pores of a flexible metal organic framework.

Authors:  Khalid A H Alzahrani; Robert J Deeth
Journal:  J Mol Model       Date:  2016-03-15       Impact factor: 1.810

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