Machine-Learning Force Field Based Computer Simulation of Rich Physical Phase Behaviour of Two-Dimensional Water/Ice in Nano-Confinement

Project: Research

Research Output

  1. 2024
  2. Published

    Rich proton dynamics and phase behaviours of nanoconfined ices

    Jiang, J., Gao, Y., Li, L., Liu, Y., Zhu, W., Zhu, C., Francisco, J. S., & 1 othersZeng, X. C., Mar 2024, In: Nature Physics. 20, 3, p. 456-464

    Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

    Scopus citations: 4
    Check@CityULib
  3. 2023
  4. Published

    Resolving Temperature-Dependent Hydrate Nucleation Pathway: The Role of “Transition Layer”

    Li, L., Wang, X., Yan, Y., Francisco, J. S., Zhang, J., Zeng, X. C. & Zhong, J., 8 Nov 2023, In: Journal of the American Chemical Society. 145, 44, p. 24166-24174

    Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

    Check@CityULib
  5. Published

    Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field

    Lin, B., Jiang, J., Zeng, X. C. & Li, L., 2023, In: Nature Communications. 14, 4110.

    Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

    Scopus citations: 9
    Check@CityULib