These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

205 related articles for article (PubMed ID: 37791097)

  • 1. Efficient Exploration of Adsorption Space for Separations in Metal-Organic Frameworks Combining the Use of Molecular Simulations, Machine Learning, and Ideal Adsorbed Solution Theory.
    Yu X; Tang D; Chng JY; Sholl DS
    J Phys Chem C Nanomater Interfaces; 2023 Sep; 127(38):19229-19239. PubMed ID: 37791097
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Adsorption-Based Separation of Near-Azeotropic Mixtures-A Challenging Example for High-Throughput Development of Adsorbents.
    Tang D; Gharagheizi F; Sholl DS
    J Phys Chem B; 2021 Jan; 125(3):926-936. PubMed ID: 33448857
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Modeling adsorption in metal-organic frameworks with open metal sites: propane/propylene separations.
    Fischer M; Gomes JR; Fröba M; Jorge M
    Langmuir; 2012 Jun; 28(22):8537-49. PubMed ID: 22574969
    [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. 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]  

  • 6. Machine learning potential for modelling H
    Liu S; Dupuis R; Fan D; Benzaria S; Bonneau M; Bhatt P; Eddaoudi M; Maurin G
    Chem Sci; 2024 Apr; 15(14):5294-5302. PubMed ID: 38577379
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs.
    Daglar H; Keskin S
    ACS Appl Mater Interfaces; 2022 Jul; 14(28):32134-32148. PubMed ID: 35818710
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Testing the accuracy of correlations for multicomponent mass transport of adsorbed gases in metal-organic frameworks: diffusion of H2/CH4 mixtures in CuBTC.
    Keskin S; Liu J; Johnson JK; Sholl DS
    Langmuir; 2008 Aug; 24(15):8254-61. PubMed ID: 18613712
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Efficiently Exploring Adsorption Space to Identify Privileged Adsorbents for Chemical Separations of a Diverse Set of Molecules.
    Tang D; Wu Y; Verploegh RJ; Sholl DS
    ChemSusChem; 2018 May; 11(9):1567-1575. PubMed ID: 29624911
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Incorporating Flexibility Effects into Metal-Organic Framework Adsorption Simulations Using Different Models.
    Yu Z; Anstine DM; Boulfelfel SE; Gu C; Colina CM; Sholl DS
    ACS Appl Mater Interfaces; 2021 Dec; 13(51):61305-61315. PubMed ID: 34927436
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Machine learning insights into predicting biogas separation in metal-organic frameworks.
    Cooley I; Boobier S; Hirst JD; Besley E
    Commun Chem; 2024 May; 7(1):102. PubMed ID: 38720065
    [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. Combination of High-Throughput Screening and Assembly to Discover Efficient Metal-Organic Frameworks on Kr/Xe Adsorption Separation.
    Du XM; Xiao ST; Wang X; Sun X; Lin YF; Wang Q; Chen GH
    J Phys Chem B; 2023 Sep; 127(38):8116-8130. PubMed ID: 37725055
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Machine Learning Models for Predicting Molecular Diffusion in Metal-Organic Frameworks Accounting for the Impact of Framework Flexibility.
    Yang Y; Yu Z; Sholl DS
    Chem Mater; 2023 Dec; 35(23):10156-10168. PubMed ID: 38107189
    [TBL] [Abstract][Full Text] [Related]  

  • 18. XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr.
    Liang H; Jiang K; Yan TA; Chen GH
    ACS Omega; 2021 Apr; 6(13):9066-9076. PubMed ID: 33842776
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Accelerated screening and assembly of promising MOFs with open Cu sites for isobutene/isobutane separation using a data-driven approach.
    Sun X; Lin W; Jiang K; Liang H; Chen G
    Phys Chem Chem Phys; 2023 Mar; 25(12):8608-8623. PubMed ID: 36891889
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Evaluating CH
    Gulbalkan HC; Uzun A; Keskin S
    ACS Appl Mater Interfaces; 2023 Dec; ():. PubMed ID: 38082488
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.