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 *

286 related articles for article (PubMed ID: 26584096)

  • 1. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.
    Hansen K; Montavon G; Biegler F; Fazli S; Rupp M; Scheffler M; von Lilienfeld OA; Tkatchenko A; Müller KR
    J Chem Theory Comput; 2013 Aug; 9(8):3404-19. PubMed ID: 26584096
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations.
    Dral PO; von Lilienfeld OA; Thiel W
    J Chem Theory Comput; 2015 May; 11(5):2120-2125. PubMed ID: 26146493
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Fast and accurate modeling of molecular atomization energies with machine learning.
    Rupp M; Tkatchenko A; Müller KR; von Lilienfeld OA
    Phys Rev Lett; 2012 Feb; 108(5):058301. PubMed ID: 22400967
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Bond Type Restricted Property Weighted Radial Distribution Functions for Accurate Machine Learning Prediction of Atomization Energies.
    Krykunov M; Woo TK
    J Chem Theory Comput; 2018 Oct; 14(10):5229-5237. PubMed ID: 30148628
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Proceedings of the Second Workshop on Theory meets Industry (Erwin-Schrödinger-Institute (ESI), Vienna, Austria, 12-14 June 2007).
    Hafner J
    J Phys Condens Matter; 2008 Feb; 20(6):060301. PubMed ID: 21693862
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Energy correctors for accurate prediction of molecular energies.
    Seminario JM; Maffei MG; Agapito LA; Salazar PF
    J Phys Chem A; 2006 Jan; 110(3):1060-4. PubMed ID: 16420008
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.
    Faber FA; Hutchison L; Huang B; Gilmer J; Schoenholz SS; Dahl GE; Vinyals O; Kearnes S; Riley PF; von Lilienfeld OA
    J Chem Theory Comput; 2017 Nov; 13(11):5255-5264. PubMed ID: 28926232
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships.
    Janet JP; Kulik HJ
    J Phys Chem A; 2017 Nov; 121(46):8939-8954. PubMed ID: 29095620
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Quantum-chemical insights from deep tensor neural networks.
    Schütt KT; Arbabzadah F; Chmiela S; Müller KR; Tkatchenko A
    Nat Commun; 2017 Jan; 8():13890. PubMed ID: 28067221
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Learning molecular energies using localized graph kernels.
    Ferré G; Haut T; Barros K
    J Chem Phys; 2017 Mar; 146(11):114107. PubMed ID: 28330348
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Ab initio machine learning of phase space averages.
    Weinreich J; Lemm D; von Rudorff GF; von Lilienfeld OA
    J Chem Phys; 2022 Jul; 157(2):024303. PubMed ID: 35840379
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine learning of accurate energy-conserving molecular force fields.
    Chmiela S; Tkatchenko A; Sauceda HE; Poltavsky I; Schütt KT; Müller KR
    Sci Adv; 2017 May; 3(5):e1603015. PubMed ID: 28508076
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited.
    Zaspel P; Huang B; Harbrecht H; von Lilienfeld OA
    J Chem Theory Comput; 2019 Mar; 15(3):1546-1559. PubMed ID: 30516999
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels.
    Dral PO; Owens A; Yurchenko SN; Thiel W
    J Chem Phys; 2017 Jun; 146(24):244108. PubMed ID: 28668062
    [TBL] [Abstract][Full Text] [Related]  

  • 18. An orbital-based representation for accurate quantum machine learning.
    Karandashev K; von Lilienfeld OA
    J Chem Phys; 2022 Mar; 156(11):114101. PubMed ID: 35317562
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Benchmark all-electron ab initio quantum Monte Carlo calculations for small molecules.
    Nemec N; Towler MD; Needs RJ
    J Chem Phys; 2010 Jan; 132(3):034111. PubMed ID: 20095732
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Spin-dependent gradient correction for more accurate atomization energies of molecules.
    Constantin LA; Fabiano E; Della Sala F
    J Chem Phys; 2012 Nov; 137(19):194105. PubMed ID: 23181292
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.