Hi, I’m Jie Huang. Welcome to my site! I’m passionate about understanding physics through tools like computer simulations and machine learning. My past comprehensive articles cover topics in machine learning, information theory, physics, and more, reflecting my ongoing research journey. Documenting my work helps me deepen my understanding, and I hope it benefits others as well. I look forward to sharing my work and discussing with you. Cheers!

News and events#

Publications#

  • Jie Huang, Niko Oinonen, Fabio Priante, Filippo Federici Canova, Lauri Kurki, Chen Xu, and Adam S. Foster*, Improving atomic force microscopy structure discovery via style-translation, arXiv 2025.

  • Jonas Heggemann, Jie Huang, Simon Aeschlimann, Simon Spiller, Adam S Foster*, and Philipp Rahe*, Sidestepping Intermolecular Hydrogen Bonds: How Single Water Molecules Adsorb and Assemble on the Calcite (104)-(2x1) Surface, ACS Nano 2025.

  • Gang Huang and Jie Huang*, Revisiting the thickness of the air-water interface from two extremes of interface hydrogen bond dynamics, J. Chem. Theory Comput. 2024. Codes

  • Jie Huang, Bowen Wang, Hejin Yan and Yongqing Cai*, Mechanism of interaction of water above MAPbI3 perovskite nanocluster, J. Phys. Chem. Lett. 15, 2024. Codes

  • Jie Huang, Gang Huang*, and Shiben Li*, A machine learning model to classify dynamic processes in liquid water, ChemPhysChem, 23, 2022.

  • Jie Huang, Shiben Li*, Xinghua Zhang*, and Gang Huang, Neural network model for structure factor of polymer systems, J. Chem. Phys. 150, 2020. Codes

Contents#

Updates#

  • 2025.05.07 Our website moves to Sphinx.

  • 2024.08.17 Our website has been redesigned using Hugo as the generator, with inspiration from TeXify3 and KaTeX.

  • 2021.10.23 An Online Forum based on Github Discussions is added to the top right corner of website.

  • 2019.01.31 Way To Machine Learning is launched in Chengdu, China.