140 related articles for article (PubMed ID: 32017547)
1. 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]
2. 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]
3. 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]
4. 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]
5. Molecular Building Block-Based Electronic Charges for High-Throughput Screening of Metal-Organic Frameworks for Adsorption Applications.
Argueta E; Shaji J; Gopalan A; Liao P; Snurr RQ; Gómez-Gualdrón DA
J Chem Theory Comput; 2018 Jan; 14(1):365-376. PubMed ID: 29227644
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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]
9. Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage.
Fanourgakis GS; Gkagkas K; Froudakis G
J Chem Phys; 2022 Feb; 156(5):054103. PubMed ID: 35135256
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. MOFormer: Self-Supervised Transformer Model for Metal-Organic Framework Property Prediction.
Cao Z; Magar R; Wang Y; Barati Farimani A
J Am Chem Soc; 2023 Feb; 145(5):2958-2967. PubMed ID: 36706365
[TBL] [Abstract][Full Text] [Related]
12. Large-scale screening of hypothetical metal-organic frameworks.
Wilmer CE; Leaf M; Lee CY; Farha OK; Hauser BG; Hupp JT; Snurr RQ
Nat Chem; 2011 Nov; 4(2):83-9. PubMed ID: 22270624
[TBL] [Abstract][Full Text] [Related]
13. Reliably Modeling the Mechanical Stability of Rigid and Flexible Metal-Organic Frameworks.
Rogge SMJ; Waroquier M; Van Speybroeck V
Acc Chem Res; 2018 Jan; 51(1):138-148. PubMed ID: 29155552
[TBL] [Abstract][Full Text] [Related]
14. DFT-Quality Adsorption Simulations in Metal-Organic Frameworks Enabled by Machine Learning Potentials.
Goeminne R; Vanduyfhuys L; Van Speybroeck V; Verstraelen T
J Chem Theory Comput; 2023 Sep; 19(18):6313-6325. PubMed ID: 37642314
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. 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]
18. Site Isolation in Metal-Organic Frameworks Enables Novel Transition Metal Catalysis.
Drake T; Ji P; Lin W
Acc Chem Res; 2018 Sep; 51(9):2129-2138. PubMed ID: 30129753
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Tuning the topology and functionality of metal-organic frameworks by ligand design.
Zhao D; Timmons DJ; Yuan D; Zhou HC
Acc Chem Res; 2011 Feb; 44(2):123-33. PubMed ID: 21126015
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
