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 *

198 related articles for article (PubMed ID: 34800941)

  • 21. Dataset of bond enthalpies (ε
    Miracle D; Dahlman A; Wilks G; Dahlman JE
    Data Brief; 2021 Dec; 39():107652. PubMed ID: 34926736
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Ab initio molecular-dynamics simulation of liquid As(x)Te(1-x) alloys.
    Zhu XF; Chen LF
    J Phys Condens Matter; 2009 Jul; 21(27):275602. PubMed ID: 21828496
    [TBL] [Abstract][Full Text] [Related]  

  • 23. On the transferability of classical pairwise additive atomistic force field to the description of unary and multi-component systems: applications to the solidification of Al-based alloys.
    Castillo-Sánchez JR; Rincent A; Gheribi AE; Harvey JP
    Phys Chem Chem Phys; 2022 Sep; 24(37):22605-22623. PubMed ID: 36102884
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Origin of pressure-induced crystallization of Ce75Al25 metallic glass.
    Wu M; Tse JS; Wang SY; Wang CZ; Jiang JZ
    Nat Commun; 2015 Mar; 6():6493. PubMed ID: 25751790
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Accelerating Elastic Property Prediction in Fe-C Alloys through Coupling of Molecular Dynamics and Machine Learning.
    Risal S; Singh N; Yao Y; Sun L; Risal S; Zhu W
    Materials (Basel); 2024 Jan; 17(3):. PubMed ID: 38591477
    [TBL] [Abstract][Full Text] [Related]  

  • 26. AisNet: A Universal Interatomic Potential Neural Network with Encoded Local Environment Features.
    Hu Z; Guo Y; Liu Z; Shi D; Li Y; Hu Y; Bu M; Luo K; He J; Wang C; Du S
    J Chem Inf Model; 2023 Mar; 63(6):1756-1765. PubMed ID: 36897781
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Benchmarking structural evolution methods for training of machine learned interatomic potentials.
    Waters MJ; Rondinelli JM
    J Phys Condens Matter; 2022 Jul; 34(38):. PubMed ID: 35797983
    [TBL] [Abstract][Full Text] [Related]  

  • 28. First-principles study of the structural and dynamic properties of the liquid and amorphous Li-Si alloys.
    Chiang HH; Lu JM; Kuo CL
    J Chem Phys; 2016 Jan; 144(3):034502. PubMed ID: 26801036
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys.
    Khalaj O; Ghobadi M; Saebnoori E; Zarezadeh A; Shishesaz M; Mašek B; Štadler C; Svoboda J
    Materials (Basel); 2021 Nov; 14(21):. PubMed ID: 34772239
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Molecular Dynamics Simulation of Zinc Ion in Water with an ab Initio Based Neural Network Potential.
    Xu M; Zhu T; Zhang JZH
    J Phys Chem A; 2019 Aug; 123(30):6587-6595. PubMed ID: 31294560
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model.
    Liu J; Zhang X; Chen T; Zhang Y; Zhang D; Zhang L; Chen M
    J Chem Theory Comput; 2024 Jul; 20(13):5717-5731. PubMed ID: 38898771
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Polyamorphism in a metallic glass.
    Sheng HW; Liu HZ; Cheng YQ; Wen J; Lee PL; Luo WK; Shastri SD; Ma E
    Nat Mater; 2007 Mar; 6(3):192-7. PubMed ID: 17310140
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Negative expansions of interatomic distances in metallic melts.
    Lou H; Wang X; Cao Q; Zhang D; Zhang J; Hu T; Mao HK; Jiang JZ
    Proc Natl Acad Sci U S A; 2013 Jun; 110(25):10068-72. PubMed ID: 23733928
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Thermodynamic and Transport Properties of LiF and FLiBe Molten Salts with Deep Learning Potentials.
    Rodriguez A; Lam S; Hu M
    ACS Appl Mater Interfaces; 2021 Nov; 13(46):55367-55379. PubMed ID: 34767334
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab initio simulations.
    Chtchelkatchev NM; Ryltsev RE; Magnitskaya MV; Gorbunov SM; Cherednichenko KA; Solozhenko VL; Brazhkin VV
    J Chem Phys; 2023 Aug; 159(6):. PubMed ID: 37551816
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks.
    Ruza J; Wang W; Schwalbe-Koda D; Axelrod S; Harris WH; Gómez-Bombarelli R
    J Chem Phys; 2020 Oct; 153(16):164501. PubMed ID: 33138411
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Structure and dynamical properties of liquid Ni
    Zhang WB; Wang XD; Cao QP; Zhang DX; Fecht HJ; Jiang JZ
    J Phys Condens Matter; 2018 Sep; 30(36):365401. PubMed ID: 30063217
    [TBL] [Abstract][Full Text] [Related]  

  • 38. A computational study of diffusion in a glass-forming metallic liquid.
    Wang T; Zhang F; Yang L; Fang XW; Zhou SH; Kramer MJ; Wang CZ; Ho KM; Napolitano RE
    Sci Rep; 2015 Jun; 5():10956. PubMed ID: 26055394
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?
    Balabin RM; Lomakina EI
    Phys Chem Chem Phys; 2011 Jun; 13(24):11710-8. PubMed ID: 21594265
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments.
    Zaverkin V; Holzmüller D; Steinwart I; Kästner J
    J Chem Theory Comput; 2021 Oct; 17(10):6658-6670. PubMed ID: 34585927
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 10.