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 *

435 related articles for article (PubMed ID: 32940999)

  • 1. Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration.
    Kang PL; Shang C; Liu ZP
    Acc Chem Res; 2020 Oct; 53(10):2119-2129. PubMed ID: 32940999
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Material discovery by combining stochastic surface walking global optimization with a neural network.
    Huang SD; Shang C; Zhang XJ; Liu ZP
    Chem Sci; 2017 Sep; 8(9):6327-6337. PubMed ID: 29308174
    [TBL] [Abstract][Full Text] [Related]  

  • 4. BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules.
    Laghuvarapu S; Pathak Y; Priyakumar UD
    J Comput Chem; 2020 Mar; 41(8):790-799. PubMed ID: 31845368
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.
    Zhang P; Yang W
    J Chem Phys; 2023 Jul; 159(2):. PubMed ID: 37431910
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Machine learning builds full-QM precision protein force fields in seconds.
    Han Y; Wang Z; Wei Z; Liu J; Li J
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34017993
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Look Inside the Black Box of Machine Learning Photodynamics Simulations.
    Li J; Lopez SA
    Acc Chem Res; 2022 Jul; 55(14):1972-1984. PubMed ID: 35796602
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks.
    Shen L; Yang W
    J Chem Theory Comput; 2018 Mar; 14(3):1442-1455. PubMed ID: 29438614
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.
    Varughese B; Manna S; Loeffler TD; Batra R; Cherukara MJ; Sankaranarayanan SKRS
    ACS Appl Mater Interfaces; 2024 Apr; ():. PubMed ID: 38593033
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Reaction prediction via atomistic simulation: from quantum mechanics to machine learning.
    Kang PL; Liu ZP
    iScience; 2021 Jan; 24(1):102013. PubMed ID: 33490920
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Construction of high-dimensional neural network potentials using environment-dependent atom pairs.
    Jose KV; Artrith N; Behler J
    J Chem Phys; 2012 May; 136(19):194111. PubMed ID: 22612084
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Neural Network Potential Energy Surfaces for Small Molecules and Reactions.
    Manzhos S; Carrington T
    Chem Rev; 2021 Aug; 121(16):10187-10217. PubMed ID: 33021368
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Theoretical studies on triplet-state driven dissociation of formaldehyde by quasi-classical molecular dynamics simulation on machine-learning potential energy surface.
    Lin S; Peng D; Yang W; Gu FL; Lan Z
    J Chem Phys; 2021 Dec; 155(21):214105. PubMed ID: 34879677
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks.
    Sun G; Sautet P
    J Chem Theory Comput; 2019 Oct; 15(10):5614-5627. PubMed ID: 31465216
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules.
    Vassilev-Galindo V; Fonseca G; Poltavsky I; Tkatchenko A
    J Chem Phys; 2021 Mar; 154(9):094119. PubMed ID: 33685131
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A novel approach to describe chemical environments in high-dimensional neural network potentials.
    Kocer E; Mason JK; Erturk H
    J Chem Phys; 2019 Apr; 150(15):154102. PubMed ID: 31005106
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Neural network potentials for chemistry: concepts, applications and prospects.
    Käser S; Vazquez-Salazar LI; Meuwly M; Töpfer K
    Digit Discov; 2023 Feb; 2(1):28-58. PubMed ID: 36798879
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations.
    Behler J
    Phys Chem Chem Phys; 2011 Oct; 13(40):17930-55. PubMed ID: 21915403
    [TBL] [Abstract][Full Text] [Related]  

  • 20. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems.
    Behler J
    Angew Chem Int Ed Engl; 2017 Oct; 56(42):12828-12840. PubMed ID: 28520235
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 22.