558 related articles for article (PubMed ID: 28800219)
21. Machine Learning Prediction on Properties of Nanoporous Materials Utilizing Pore Geometry Barcodes.
Zhang X; Cui J; Zhang K; Wu J; Lee Y
J Chem Inf Model; 2019 Nov; 59(11):4636-4644. PubMed ID: 31661958
[TBL] [Abstract][Full Text] [Related]
22. Rapid Screening of Metal-Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning.
Tang H; Xu Q; Wang M; Jiang J
ACS Appl Mater Interfaces; 2021 Nov; 13(45):53454-53467. PubMed ID: 34665615
[TBL] [Abstract][Full Text] [Related]
23. A method for screening the potential of MOFs as CO2 adsorbents in pressure swing adsorption processes.
Pirngruber GD; Hamon L; Bourrelly S; Llewellyn PL; Lenoir E; Guillerm V; Serre C; Devic T
ChemSusChem; 2012 Apr; 5(4):762-76. PubMed ID: 22438338
[TBL] [Abstract][Full Text] [Related]
24. Porous M(II)/pyrimidine-4,6-dicarboxylato neutral frameworks: synthetic influence on the adsorption capacity and evaluation of CO2-adsorbent interactions.
Cepeda J; Pérez-Yáñez S; Beobide G; Castillo O; Fischer M; Luque A; Wright PA
Chemistry; 2014 Feb; 20(6):1554-68. PubMed ID: 24403128
[TBL] [Abstract][Full Text] [Related]
25. Modulating adsorption and stability properties in pillared metal-organic frameworks: a model system for understanding ligand effects.
Burtch NC; Walton KS
Acc Chem Res; 2015 Nov; 48(11):2850-7. PubMed ID: 26529060
[TBL] [Abstract][Full Text] [Related]
26. The application of machine learning for predicting the methane uptake and working capacity of MOFs.
Suyetin M
Faraday Discuss; 2021 Oct; 231(0):224-234. PubMed ID: 34195741
[TBL] [Abstract][Full Text] [Related]
27. Optimized synthesis and crystalline stability of γ-cyclodextrin metal-organic frameworks for drug adsorption.
Liu B; Li H; Xu X; Li X; Lv N; Singh V; Stoddart JF; York P; Xu X; Gref R; Zhang J
Int J Pharm; 2016 Nov; 514(1):212-219. PubMed ID: 27863664
[TBL] [Abstract][Full Text] [Related]
28. Accelerating In Silico Discovery of Metal-Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning.
Daoo V; Singh JK
ACS Appl Mater Interfaces; 2024 Feb; 16(6):6971-6987. PubMed ID: 38289235
[TBL] [Abstract][Full Text] [Related]
29. Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture.
Fernandez M; Boyd PG; Daff TD; Aghaji MZ; Woo TK
J Phys Chem Lett; 2014 Sep; 5(17):3056-60. PubMed ID: 26278259
[TBL] [Abstract][Full Text] [Related]
30. Advancing CH
Aksu GO; Keskin S
J Mater Chem A Mater; 2023 Jul; 11(27):14788-14799. PubMed ID: 37441278
[TBL] [Abstract][Full Text] [Related]
31. Using surrogate modeling in the prediction of fibrinogen adsorption onto polymer surfaces.
Smith JR; Knight D; Kohn J; Rasheed K; Weber N; Kholodovych V; Welsh WJ
J Chem Inf Comput Sci; 2004; 44(3):1088-97. PubMed ID: 15154777
[TBL] [Abstract][Full Text] [Related]
32. Machine-learning-driven discovery of metal-organic framework adsorbents for hexavalent chromium removal from aqueous environments.
Jiang M; Fu W; Wang Y; Xu D; Wang S
J Colloid Interface Sci; 2024 May; 662():836-845. PubMed ID: 38382368
[TBL] [Abstract][Full Text] [Related]
33. Gradient Boosted Machine Learning Model to Predict H
Bailey T; Jackson A; Berbece RA; Wu K; Hondow N; Martin E
J Chem Inf Model; 2023 Aug; 63(15):4545-4551. PubMed ID: 37463276
[TBL] [Abstract][Full Text] [Related]
34. Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood-brain Barrier Entry of Chemical Compounds Using Machine Learning.
Majumdar S; Basak SC; Lungu CN; Diudea MV; Grunwald GD
Mol Inform; 2019 Aug; 38(8-9):e1800164. PubMed ID: 31322827
[TBL] [Abstract][Full Text] [Related]
35. Screening of Covalent-Organic Frameworks for Adsorption Heat Pumps.
Li W; Xia X; Li S
ACS Appl Mater Interfaces; 2020 Jan; 12(2):3265-3273. PubMed ID: 31865693
[TBL] [Abstract][Full Text] [Related]
36. High efficiency adsorption and removal of selenate and selenite from water using metal-organic frameworks.
Howarth AJ; Katz MJ; Wang TC; Platero-Prats AE; Chapman KW; Hupp JT; Farha OK
J Am Chem Soc; 2015 Jun; 137(23):7488-94. PubMed ID: 26000611
[TBL] [Abstract][Full Text] [Related]
37. Intelligent Selection of Metal-Organic Framework Arrays for Methane Sensing via Genetic Algorithms.
Gustafson JA; Wilmer CE
ACS Sens; 2019 Jun; 4(6):1586-1593. PubMed ID: 31124354
[TBL] [Abstract][Full Text] [Related]
38. Machine learning using host/guest energy histograms to predict adsorption in metal-organic frameworks: Application to short alkanes and Xe/Kr mixtures.
Li Z; Bucior BJ; Chen H; Haranczyk M; Siepmann JI; Snurr RQ
J Chem Phys; 2021 Jul; 155(1):014701. PubMed ID: 34241399
[TBL] [Abstract][Full Text] [Related]
39. Computational Screening of Metal⁻Organic Framework Membranes for the Separation of 15 Gas Mixtures.
Yang W; Liang H; Peng F; Liu Z; Liu J; Qiao Z
Nanomaterials (Basel); 2019 Mar; 9(3):. PubMed ID: 30897779
[TBL] [Abstract][Full Text] [Related]
40. Transfer Learning Study of Gas Adsorption in Metal-Organic Frameworks.
Ma R; Colón YJ; Luo T
ACS Appl Mater Interfaces; 2020 Jul; 12(30):34041-34048. PubMed ID: 32613831
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]