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 *

117 related articles for article (PubMed ID: 39225311)

  • 1. A machine learning potential construction based on radial distribution function sampling.
    Watanabe N; Hori Y; Sugisawa H; Ida T; Shoji M; Shigeta Y
    J Comput Chem; 2024 Dec; 45(32):2949-2958. PubMed ID: 39225311
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment.
    Matin S; Allen AEA; Smith J; Lubbers N; Jadrich RB; Messerly R; Nebgen B; Li YW; Tretiak S; Barros K
    J Chem Theory Comput; 2024 Feb; 20(3):1274-1281. PubMed ID: 38307009
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Machine Learning of Reactive Potentials.
    Yang Y; Zhang S; Ranasinghe KD; Isayev O; Roitberg AE
    Annu Rev Phys Chem; 2024 Jun; 75(1):371-395. PubMed ID: 38941524
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations.
    Yao S; Van R; Pan X; Park JH; Mao Y; Pu J; Mei Y; Shao Y
    RSC Adv; 2023 Jan; 13(7):4565-4577. PubMed ID: 36760282
    [TBL] [Abstract][Full Text] [Related]  

  • 5. AENET-LAMMPS and AENET-TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials.
    Chen MS; Morawietz T; Mori H; Markland TE; Artrith N
    J Chem Phys; 2021 Aug; 155(7):074801. PubMed ID: 34418919
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Active sparse Bayesian committee machine potential for isothermal-isobaric molecular dynamics simulations.
    Willow SY; Kim DG; Sundheep R; Hajibabaei A; Kim KS; Myung CW
    Phys Chem Chem Phys; 2024 Aug; 26(33):22073-22082. PubMed ID: 39113586
    [TBL] [Abstract][Full Text] [Related]  

  • 7. General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer.
    Ko TW; Finkler JA; Goedecker S; Behler J
    Acc Chem Res; 2021 Feb; 54(4):808-817. PubMed ID: 33513012
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions.
    Pan X; Yang J; Van R; Epifanovsky E; Ho J; Huang J; Pu J; Mei Y; Nam K; Shao Y
    J Chem Theory Comput; 2021 Sep; 17(9):5745-5758. PubMed ID: 34468138
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Accelerating Metadynamics-Based Free-Energy Calculations with Adaptive Machine Learning Potentials.
    Xu J; Cao XM; Hu P
    J Chem Theory Comput; 2021 Jul; 17(7):4465-4476. PubMed ID: 34100605
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Toward an ab Initio Description of Adsorbate Surface Dynamics.
    Sivakumar S; Kulkarni A
    J Phys Chem C Nanomater Interfaces; 2024 Aug; 128(31):13238-13248. PubMed ID: 39140094
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Machine learning transferable atomic forces for large systems from underconverged molecular fragments.
    Herbold M; Behler J
    Phys Chem Chem Phys; 2023 May; 25(18):12979-12989. PubMed ID: 37165873
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Local-environment-guided selection of atomic structures for the development of machine-learning potentials.
    Li R; Zhou C; Singh A; Pei Y; Henkelman G; Li L
    J Chem Phys; 2024 Feb; 160(7):. PubMed ID: 38380745
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Ab initio molecular dynamics of hydrogen dissociation on metal surfaces using neural networks and novelty sampling.
    Ludwig J; Vlachos DG
    J Chem Phys; 2007 Oct; 127(15):154716. PubMed ID: 17949200
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Anisotropic Collective Variables with Machine Learning Potential for
    Deng Y; Fu S; Guo J; Xu X; Li H
    ACS Nano; 2023 Jul; 17(14):14099-14113. PubMed ID: 37458408
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials.
    Avula NVS; Klein ML; Balasubramanian S
    J Phys Chem Lett; 2023 Oct; 14(42):9500-9507. PubMed ID: 37851540
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Solvation Free Energy Calculations with Quantum Mechanics/Molecular Mechanics and Machine Learning Models.
    Zhang P; Shen L; Yang W
    J Phys Chem B; 2019 Jan; 123(4):901-908. PubMed ID: 30557020
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Active Learning of Boltzmann Samplers and Potential Energies with Quantum Mechanical Accuracy.
    Molina-Taborda A; Cossio P; Lopez-Acevedo O; Gabrié M
    J Chem Theory Comput; 2024 Oct; 20(20):8833-8843. PubMed ID: 39370622
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage.
    Cui S; Luo Y; Tseng HH; Ten Haken RK; El Naqa I
    Med Phys; 2019 May; 46(5):2497-2511. PubMed ID: 30891794
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations.
    Van Speybroeck V; Bocus M; Cnudde P; Vanduyfhuys L
    ACS Catal; 2023 Sep; 13(17):11455-11493. PubMed ID: 37671178
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.