264 related articles for article (PubMed ID: 31661958)
1. 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]
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. Analysis of CH
Gülsoy Z; Sezginel KB; Uzun A; Keskin S; Yıldırım R
ACS Comb Sci; 2019 Apr; 21(4):257-268. PubMed ID: 30821957
[TBL] [Abstract][Full Text] [Related]
5. Molecular simulations for energy, environmental and pharmaceutical applications of nanoporous materials: from zeolites, metal-organic frameworks to protein crystals.
Jiang J; Babarao R; Hu Z
Chem Soc Rev; 2011 Jul; 40(7):3599-612. PubMed ID: 21512695
[TBL] [Abstract][Full Text] [Related]
6. A data-science approach to predict the heat capacity of nanoporous materials.
Moosavi SM; Novotny BÁ; Ongari D; Moubarak E; Asgari M; Kadioglu Ö; Charalambous C; Ortega-Guerrero A; Farmahini AH; Sarkisov L; Garcia S; Noé F; Smit B
Nat Mater; 2022 Dec; 21(12):1419-1425. PubMed ID: 36229651
[TBL] [Abstract][Full Text] [Related]
7. Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior.
Hardiagon A; Coudert FX
Acc Chem Res; 2024 Jun; 57(11):1620-1632. PubMed ID: 38752454
[TBL] [Abstract][Full Text] [Related]
8. MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes.
Kim S; Jinich A; Aspuru-Guzik A
J Chem Inf Model; 2017 Apr; 57(4):657-668. PubMed ID: 28328209
[TBL] [Abstract][Full Text] [Related]
9. High-Throughput Screening Approach for Nanoporous Materials Genome Using Topological Data Analysis: Application to Zeolites.
Lee Y; Barthel SD; Dłotko P; Moosavi SM; Hess K; Smit B
J Chem Theory Comput; 2018 Aug; 14(8):4427-4437. PubMed ID: 29986145
[TBL] [Abstract][Full Text] [Related]
10. Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design.
Janet JP; Duan C; Nandy A; Liu F; Kulik HJ
Acc Chem Res; 2021 Feb; 54(3):532-545. PubMed ID: 33480674
[TBL] [Abstract][Full Text] [Related]
11. Machine Learning Enabled Tailor-Made Design of Application-Specific Metal-Organic Frameworks.
Zhang X; Zhang K; Lee Y
ACS Appl Mater Interfaces; 2020 Jan; 12(1):734-743. PubMed ID: 31820913
[TBL] [Abstract][Full Text] [Related]
12. Data-driven machine learning model for the prediction of oxygen vacancy formation energy of metal oxide materials.
Wan Z; Wang QD; Liu D; Liang J
Phys Chem Chem Phys; 2021 Jul; 23(29):15675-15684. PubMed ID: 34269780
[TBL] [Abstract][Full Text] [Related]
13. Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.
Jablonka KM; Ongari D; Moosavi SM; Smit B
Chem Rev; 2020 Aug; 120(16):8066-8129. PubMed ID: 32520531
[TBL] [Abstract][Full Text] [Related]
14. Theoretical maximal storage of hydrogen in zeolitic frameworks.
Vitillo JG; Ricchiardi G; Spoto G; Zecchina A
Phys Chem Chem Phys; 2005 Dec; 7(23):3948-54. PubMed ID: 19810324
[TBL] [Abstract][Full Text] [Related]
15. Topological Data Analysis Combined with High-Throughput Computational Screening of Hydrophobic Metal-Organic Frameworks: Application to the Adsorptive Separation of C3 Components.
Yang Y; Guo S; Li S; Wu Y; Qiao Z
Nanomaterials (Basel); 2024 Jan; 14(3):. PubMed ID: 38334569
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning.
Luo Y; Bag S; Zaremba O; Cierpka A; Andreo J; Wuttke S; Friederich P; Tsotsalas M
Angew Chem Int Ed Engl; 2022 May; 61(19):e202200242. PubMed ID: 35104033
[TBL] [Abstract][Full Text] [Related]
18. The effect of descriptor choice in machine learning models for ionic liquid melting point prediction.
Low K; Kobayashi R; Izgorodina EI
J Chem Phys; 2020 Sep; 153(10):104101. PubMed ID: 32933305
[TBL] [Abstract][Full Text] [Related]
19. Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.
Nath A; Subbiah K
Comput Biol Chem; 2015 Dec; 59 Pt A():101-10. PubMed ID: 26433483
[TBL] [Abstract][Full Text] [Related]
20. Beyond the BET Analysis: The Surface Area Prediction of Nanoporous Materials Using a Machine Learning Method.
Datar A; Chung YG; Lin LC
J Phys Chem Lett; 2020 Jul; 11(14):5412-5417. PubMed ID: 32510221
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
