199 related articles for article (PubMed ID: 36234502)
1. Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials.
Guo W; Liu J; Dong F; Chen R; Das J; Ge W; Xu X; Hong H
Nanomaterials (Basel); 2022 Sep; 12(19):. PubMed ID: 36234502
[TBL] [Abstract][Full Text] [Related]
2. Accelerating Discovery of Metal-Organic Frameworks for Methane Adsorption with Hierarchical Screening and Deep Learning.
Wang R; Zhong Y; Bi L; Yang M; Xu D
ACS Appl Mater Interfaces; 2020 Nov; 12(47):52797-52807. PubMed ID: 33175490
[TBL] [Abstract][Full Text] [Related]
3. Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs).
Pardakhti M; Moharreri E; Wanik D; Suib SL; Srivastava R
ACS Comb Sci; 2017 Oct; 19(10):640-645. PubMed ID: 28800219
[TBL] [Abstract][Full Text] [Related]
4. Deep learning combined with IAST to screen thermodynamically feasible MOFs for adsorption-based separation of multiple binary mixtures.
Anderson R; Gómez-Gualdrón DA
J Chem Phys; 2021 Jun; 154(23):234102. PubMed ID: 34241255
[TBL] [Abstract][Full Text] [Related]
5. Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model.
Anderson R; Biong A; Gómez-Gualdrón DA
J Chem Theory Comput; 2020 Feb; 16(2):1271-1283. PubMed ID: 31922755
[TBL] [Abstract][Full Text] [Related]
6. Interpretable Graph Transformer Network for Predicting Adsorption Isotherms of Metal-Organic Frameworks.
Chen P; Jiao R; Liu J; Liu Y; Lu Y
J Chem Inf Model; 2022 Nov; 62(22):5446-5456. PubMed ID: 36318767
[TBL] [Abstract][Full Text] [Related]
7. Data Driven Discovery of MOFs for Hydrogen Gas Adsorption.
Singh SK; Sose AT; Wang F; Bejagam KK; Deshmukh SA
J Chem Theory Comput; 2023 Oct; 19(19):6686-6703. PubMed ID: 37756641
[TBL] [Abstract][Full Text] [Related]
8. A Universal Machine Learning Algorithm for Large-Scale Screening of Materials.
Fanourgakis GS; Gkagkas K; Tylianakis E; Froudakis GE
J Am Chem Soc; 2020 Feb; 142(8):3814-3822. PubMed ID: 32017547
[TBL] [Abstract][Full Text] [Related]
9. Geometrical Properties Can Predict CO2 and N2 Adsorption Performance of Metal-Organic Frameworks (MOFs) at Low Pressure.
Fernandez M; Barnard AS
ACS Comb Sci; 2016 May; 18(5):243-52. PubMed ID: 27022760
[TBL] [Abstract][Full Text] [Related]
10. Evaluating CH
Gulbalkan HC; Uzun A; Keskin S
ACS Appl Mater Interfaces; 2023 Dec; ():. PubMed ID: 38082488
[TBL] [Abstract][Full Text] [Related]
11. Prediction of arsenic adsorption onto metal organic frameworks and adsorption mechanisms interpretation by machine learning.
Xiong T; Cui J; Hou Z; Yuan X; Wang H; Chen J; Yang Y; Huang Y; Xu X; Su C; Leng L
J Environ Manage; 2023 Dec; 347():119065. PubMed ID: 37801942
[TBL] [Abstract][Full Text] [Related]
12. Deep learning and big data mining for Metal-Organic frameworks with high performance for simultaneous desulfurization and carbon capture.
Guan K; Xu F; Huang X; Li Y; Guo S; Situ Y; Chen Y; Hu J; Liu Z; Liang H; Zhu X; Wu Y; Qiao Z
J Colloid Interface Sci; 2024 May; 662():941-952. PubMed ID: 38382377
[TBL] [Abstract][Full Text] [Related]
13. Exploring the Effect of Ligand-Originated MOF Isomerism and Methoxy Group Functionalization on Selective Acetylene/Methane and Carbon Dioxide/Methane Adsorption Properties in Two NbO-Type MOFs.
Wang Y; He M; Gao X; Li S; Xiong S; Krishna R; He Y
ACS Appl Mater Interfaces; 2018 Jun; 10(24):20559-20568. PubMed ID: 29856212
[TBL] [Abstract][Full Text] [Related]
14. Predicting the Features of Methane Adsorption in Large Pore Metal-Organic Frameworks for Energy Storage.
Manos G; Dunne LJ
Nanomaterials (Basel); 2018 Oct; 8(10):. PubMed ID: 30314317
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal-Organic Frameworks.
Li W; Situ Y; Ding L; Chen Y; Yang Q
ACS Appl Mater Interfaces; 2023 Dec; 15(51):59887-59894. PubMed ID: 38087435
[TBL] [Abstract][Full Text] [Related]
17. A Robust Machine Learning Algorithm for the Prediction of Methane Adsorption in Nanoporous Materials.
Fanourgakis GS; Gkagkas K; Tylianakis E; Klontzas E; Froudakis G
J Phys Chem A; 2019 Jul; 123(28):6080-6087. PubMed ID: 31264869
[TBL] [Abstract][Full Text] [Related]
18. Acetylene Storage and Separation Using Metal-Organic Frameworks with Open Metal Sites.
Luna-Triguero A; Vicent-Luna JM; Madero-Castro RM; Gómez-Álvarez P; Calero S
ACS Appl Mater Interfaces; 2019 Aug; 11(34):31499-31507. PubMed ID: 31368697
[TBL] [Abstract][Full Text] [Related]
19. In Silico Evolution of High-Performing Metal Organic Frameworks for Methane Adsorption.
Beauregard N; Pardakhti M; Srivastava R
J Chem Inf Model; 2021 Jul; 61(7):3232-3239. PubMed ID: 34264660
[TBL] [Abstract][Full Text] [Related]
20. Correlation between the Metal and Organic Components, Structure Property, and Gas-Adsorption Capacity of Metal-Organic Frameworks.
Yuyama S; Kaneko H
J Chem Inf Model; 2021 Dec; 61(12):5785-5792. PubMed ID: 34898202
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
