Zhuoran Qiao

A practitioner of Molecular Simulation, Statistical Physics, and Machine Learning.

I am a Ph.D. student at Caltech CCE working with Prof. Anima Anandkumar. I collaborate closely with colleagues from the Miller Group and Entos. My research centers around developing physics-driven geometric learning approaches to tackle complex problems in chemistry and biology, especially for the study of electronic structure and dynamics out of equilibrium.

I earned my BSc from Peking University in 2019. As an undergraduate student I worked in the Gao Group at PKU CCME, where I studied the statistical mechanics of confined soft matters.

Selected publications

  1. Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces
    Qiao, Zhuoran, Ding, Feizhi, Welborn, Matthew, Bygrave, Peter J, Anandkumar, Animashree, Manby, Frederick R, and Miller III, Thomas F
    arXiv preprint arXiv:2011.02680 2020 (Appeared at Machine Learning for Molecules workshop at NeurIPS 2020 as a contributed talk)
  2. OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
    Qiao, Zhuoran, Welborn, Matthew, Anandkumar, Animashree, Manby, Frederick R, and Miller III, Thomas F
    The Journal of Chemical Physics 2020 (Editor’s Pick)
  3. Ice nucleation of confined monolayer water conforms to classical nucleation theory
    Qiao, Zhuoran, Zhao, Yuheng, and Gao, Yi Qin
    The journal of physical chemistry letters 2019