High performance computing in materials science

INVITED · S30 · ID: 1851065

Presentations

  • Molecular Dynamics Simulation of Drying and Self-assembly

    ORAL · Invited

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    Publication: Y. Tang, G. S. Grest, and S. Cheng, Langmuir 34 (24), 7161 (2018), Stratification in drying films containing bidisperse mixtures of nanoparticles.

    Y. Tang and S. Cheng, Phys. Rev. E 98, 032802 (2018), Capillary forces on a small particle at a liquid-vapor interface: Theory and simulation.

    Y. Tang, G. S. Grest, and S. Cheng, Langmuir 35 (12), 4296 (2019), Control of stratification in drying particle suspensions via temperature gradients.

    Y. Tang, G. S. Grest, and S. Cheng, J. Chem. Phys. 150, 224901 (2019), Stratification of drying particle suspensions: Comparison of implicit and explicit solvent simulations.

    C. Wen, B. Liu, J. Wolfgang, T. E. Long, R. Odle, and S. Cheng, J. Polym. Sci. 58(11), 1521-1534 (2020), Determination of glass transition temperature of polyimides from all-atom simulations and machine-learning algorithms.

    Y. Tang, J. E. McLaughlan, G. S. Grest, and S. Cheng, Polymers 14(19), 3996 (2022), Modeling solution drying by moving a liquid-vapor interface: Method and applications.

    B. Liu, G. S. Grest, and S. Cheng, Inducing stratification of colloidal mixtures with a mixed binary solvent. (under review with Soft Matter)

    G. Davis, C. R. Grosenick, S. Dayal, M. J. Stevens, and S. Cheng, Self-assembly of artificial actin filaments. (in preparation)

    B. Liu, G. S. Grest, and S. Cheng, Molecular dynamics simulation of colloidal diffusiophoresis. (in preparation)

    J. Wang, G. Seidel, and S. Cheng, van der Waals interaction between thin rods. (in preparation)

    Presenters

    • Shengfeng Cheng

      Virginia Tech

    Authors

    • Shengfeng Cheng

      Virginia Tech

    View abstract →

  • Computational Materials Discovery for Carbon Dioxide Capture Applications

    ORAL · Invited

    –

    Publication: [1] Giro, R., Hsu, H., Kishimoto, A. et al. AI powered, automated discovery of polymer membranes for carbon capture. NPJ Comput Mater 9, 133 (2023). https://doi.org/10.1038/s41524-023-01088-3.

    [2] Ferrari, B.S., Manica, M., Giro, R. et al. Predicting polymerization reactions via transfer learning using chemical language models. arXiv Preprint (2023). https://arxiv.org/abs/2310.11423.

    [3] Oliveira, F.L., Cleeton, C., Neumann Barros Ferreira, R. et al. CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks. Sci Data 10, 230 (2023). https://doi.org/10.1038/s41597-023-02116-z.

    [4] Zheng, B., Lopes Oliveira, F., Neumann Barros Ferreira, R. et al. Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74. ACS Nano 17 (6), 5579-5587 (2023). https://doi.org/10.1021/acsnano.2c11102.

    [5] Cipcigan, F., Booth, J., Neumann Barros Ferreira, R. et al. Discovery of Novel Reticular Materials for Carbon Dioxide Capture using GflowNets. arXiv Preprint (2023). https://arxiv.org/abs/2310.07671.

    Presenters

    • Mathias B Steiner

      IBM Research - Brazil, IBM Research

    Authors

    • Mathias B Steiner

      IBM Research - Brazil, IBM Research

    View abstract →