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 *

168 related articles for article (PubMed ID: 38030720)

  • 1. Scaling deep learning for materials discovery.
    Merchant A; Batzner S; Schoenholz SS; Aykol M; Cheon G; Cubuk ED
    Nature; 2023 Dec; 624(7990):80-85. PubMed ID: 38030720
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A universal graph deep learning interatomic potential for the periodic table.
    Chen C; Ong SP
    Nat Comput Sci; 2022 Nov; 2(11):718-728. PubMed ID: 38177366
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials.
    Schmidt J; Hoffmann N; Wang HC; Borlido P; Carriço PJMA; Cerqueira TFT; Botti S; Marques MAL
    Adv Mater; 2023 Jun; 35(22):e2210788. PubMed ID: 36949007
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning.
    Sun J; Kang S; Kim J; Min K
    ACS Appl Mater Interfaces; 2023 Feb; 15(4):5049-5057. PubMed ID: 36654192
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams.
    Rossignol H; Minotakis M; Cobelli M; Sanvito S
    J Chem Inf Model; 2024 Mar; 64(6):1828-1840. PubMed ID: 38271693
    [TBL] [Abstract][Full Text] [Related]  

  • 7. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.
    Batzner S; Musaelian A; Sun L; Geiger M; Mailoa JP; Kornbluth M; Molinari N; Smidt TE; Kozinsky B
    Nat Commun; 2022 May; 13(1):2453. PubMed ID: 35508450
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting energy and stability of known and hypothetical crystals using graph neural network.
    Pandey S; Qu J; Stevanović V; St John P; Gorai P
    Patterns (N Y); 2021 Nov; 2(11):100361. PubMed ID: 34820646
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Deep Mining Stable and Nontoxic Hybrid Organic-Inorganic Perovskites for Photovoltaics via Progressive Machine Learning.
    Wu T; Wang J
    ACS Appl Mater Interfaces; 2020 Dec; 12(52):57821-57831. PubMed ID: 33325688
    [TBL] [Abstract][Full Text] [Related]  

  • 10. High-Throughput Condensed-Phase Hybrid Density Functional Theory for Large-Scale Finite-Gap Systems: The SeA Approach.
    Ko HY; Calegari Andrade MF; Sparrow ZM; Zhang JA; DiStasio RA
    J Chem Theory Comput; 2023 Jul; 19(13):4182-4201. PubMed ID: 37385014
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Neural networks for convex hull computation.
    Leung Y; Zhang JS; Xu ZB
    IEEE Trans Neural Netw; 1997; 8(3):601-11. PubMed ID: 18255663
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Gaussian approximation potentials for accurate thermal properties of two-dimensional materials.
    Kocabaş T; Keçeli M; Vázquez-Mayagoitia Á; Sevik C
    Nanoscale; 2023 May; 15(19):8772-8780. PubMed ID: 37098822
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods.
    Choudhary K; Bercx M; Jiang J; Pachter R; Lamoen D; Tavazza F
    Chem Mater; 2019; 31(15):. PubMed ID: 32165788
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation.
    Chen C; Nguyen DT; Lee SJ; Baker NA; Karakoti AS; Lauw L; Owen C; Mueller KT; Bilodeau BA; Murugesan V; Troyer M
    J Am Chem Soc; 2024 Jul; 146(29):20009-20018. PubMed ID: 38980280
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Ionic Conduction through Reaction Products at the Electrolyte-Electrode Interface in All-Solid-State Li
    Wang C; Aoyagi K; Aykol M; Mueller T
    ACS Appl Mater Interfaces; 2020 Dec; 12(49):55510-55519. PubMed ID: 33258370
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Design of New Inorganic Crystals with the Desired Composition Using Deep Learning.
    Han S; Lee J; Han S; Moosavi SM; Kim J; Park C
    J Chem Inf Model; 2023 Sep; 63(18):5755-5763. PubMed ID: 37683188
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Application of convex hull analysis for the evaluation of data heterogeneity between patient populations of different origin and implications of hospital bias in downstream machine-learning-based data processing: A comparison of 4 critical-care patient datasets.
    Sharafutdinov K; Bhat JS; Fritsch SJ; Nikulina K; E Samadi M; Polzin R; Mayer H; Marx G; Bickenbach J; Schuppert A
    Front Big Data; 2022; 5():603429. PubMed ID: 36387013
    [TBL] [Abstract][Full Text] [Related]  

  • 18. High-Throughput Screening of Promising Redox-Active Molecules with MolGAT.
    Chaka MD; Geffe CA; Rodriguez A; Seriani N; Wu Q; Mekonnen YS
    ACS Omega; 2023 Jul; 8(27):24268-24278. PubMed ID: 37457475
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Rapid discovery of stable materials by coordinate-free coarse graining.
    Goodall REA; Parackal AS; Faber FA; Armiento R; Lee AA
    Sci Adv; 2022 Jul; 8(30):eabn4117. PubMed ID: 35895811
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A Design-to-Device Pipeline for Data-Driven Materials Discovery.
    Cole JM
    Acc Chem Res; 2020 Mar; 53(3):599-610. PubMed ID: 32096410
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.