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 *

165 related articles for article (PubMed ID: 29579387)

  • 1. Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning.
    Kranz JJ; Kubillus M; Ramakrishnan R; von Lilienfeld OA; Elstner M
    J Chem Theory Comput; 2018 May; 14(5):2341-2352. PubMed ID: 29579387
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks.
    Stöhr M; Medrano Sandonas L; Tkatchenko A
    J Phys Chem Lett; 2020 Aug; 11(16):6835-6843. PubMed ID: 32787209
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression.
    Panosetti C; Engelmann A; Nemec L; Reuter K; Margraf JT
    J Chem Theory Comput; 2020 Apr; 16(4):2181-2191. PubMed ID: 32155065
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Numerical Optimization of Density Functional Tight Binding Models: Application to Molecules Containing Carbon, Hydrogen, Nitrogen, and Oxygen.
    Krishnapriyan A; Yang P; Niklasson AMN; Cawkwell MJ
    J Chem Theory Comput; 2017 Dec; 13(12):6191-6200. PubMed ID: 29039935
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Automatized parametrization of SCC-DFTB repulsive potentials: application to hydrocarbons.
    Gaus M; Chou CP; Witek H; Elstner M
    J Phys Chem A; 2009 Oct; 113(43):11866-81. PubMed ID: 19778029
    [TBL] [Abstract][Full Text] [Related]  

  • 6. The treatment of solvation by a generalized Born model and a self-consistent charge-density functional theory-based tight-binding method.
    Xie L; Liu H
    J Comput Chem; 2002 Nov; 23(15):1404-15. PubMed ID: 12370943
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Parametrization and Benchmark of Long-Range Corrected DFTB2 for Organic Molecules.
    Vuong VQ; Akkarapattiakal Kuriappan J; Kubillus M; Kranz JJ; Mast T; Niehaus TA; Irle S; Elstner M
    J Chem Theory Comput; 2018 Jan; 14(1):115-125. PubMed ID: 29232515
    [TBL] [Abstract][Full Text] [Related]  

  • 8. The limitations of Slater's element-dependent exchange functional from analytic density-functional theory.
    Zope RR; Dunlap BI
    J Chem Phys; 2006 Jan; 124(4):044107. PubMed ID: 16460149
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Transferable density functional tight binding for carbon, hydrogen, nitrogen, and oxygen: Application to shock compression.
    Cawkwell MJ; Perriot R
    J Chem Phys; 2019 Jan; 150(2):024107. PubMed ID: 30646702
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Application of the computationally efficient self-consistent-charge density-functional tight-binding method to magnesium-containing molecules.
    Cai ZL; Lopez P; Reimers JR; Cui Q; Elstner M
    J Phys Chem A; 2007 Jul; 111(26):5743-50. PubMed ID: 17555305
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Machine Learning Enhanced DFTB Method for Periodic Systems: Learning from Electronic Density of States.
    Sun W; Fan G; van der Heide T; McSloy A; Frauenheim T; Aradi B
    J Chem Theory Comput; 2023 Jul; 19(13):3877-3888. PubMed ID: 37350192
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A Caveat on SCC-DFTB and Noncovalent Interactions Involving Sulfur Atoms.
    Petraglia R; Corminboeuf C
    J Chem Theory Comput; 2013 Jul; 9(7):3020-5. PubMed ID: 26583983
    [TBL] [Abstract][Full Text] [Related]  

  • 13. DFTB Parameters for the Periodic Table, Part 2: Energies and Energy Gradients from Hydrogen to Calcium.
    Oliveira AF; Philipsen P; Heine T
    J Chem Theory Comput; 2015 Nov; 11(11):5209-18. PubMed ID: 26574316
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Automated Repulsive Parametrization for the DFTB Method.
    Bodrog Z; Aradi B; Frauenheim T
    J Chem Theory Comput; 2011 Aug; 7(8):2654-64. PubMed ID: 26606638
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space.
    Hansen K; Biegler F; Ramakrishnan R; Pronobis W; von Lilienfeld OA; Müller KR; Tkatchenko A
    J Phys Chem Lett; 2015 Jun; 6(12):2326-31. PubMed ID: 26113956
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Tight-Binding Modeling of Uranium in an Aqueous Environment.
    Carlson RK; Cawkwell MJ; Batista ER; Yang P
    J Chem Theory Comput; 2020 May; 16(5):3073-3083. PubMed ID: 32337989
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Analytical Time-Dependent Long-Range Corrected Density Functional Tight Binding (TD-LC-DFTB) Gradients in DFTB+: Implementation and Benchmark for Excited-State Geometries and Transition Energies.
    Sokolov M; Bold BM; Kranz JJ; Höfener S; Niehaus TA; Elstner M
    J Chem Theory Comput; 2021 Apr; 17(4):2266-2282. PubMed ID: 33689344
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Self-Consistent Charge Density-Functional Tight-Binding Parametrization for Pt-Ru Alloys.
    Shi H; Koskinen P; Ramasubramaniam A
    J Phys Chem A; 2017 Mar; 121(12):2497-2502. PubMed ID: 28267337
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning.
    Dandu NK; Ward L; Assary RS; Redfern PC; Curtiss LA
    J Phys Chem A; 2023 Jul; 127(28):5914-5920. PubMed ID: 37406209
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms.
    Dandu N; Ward L; Assary RS; Redfern PC; Narayanan B; Foster IT; Curtiss LA
    J Phys Chem A; 2020 Jul; 124(28):5804-5811. PubMed ID: 32539388
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.