Yi Zhang

Cornell University
Dr. Yi Zhang (Frank) is interested in various topics in theoretical condensed matter physics with the focus on but not limited to emergent phenomena in quantum many-body systems by strong correlation. 
 
  • Topological phases and materials
    • Symmetry-protected topological insulators
    • Strongly-correlated Abelian and non-Abelian topological order
    • Quantum spin liquids
    • Weyl and Dirac semi-metals

 

  • Machine learning approach to strongly-correlated systems
    • Quantum Loop Topography for machine learning 
    • Machine learning entanglement spectrum, etc.
    • Machine learning for experimental and synthetic data analysis

 

  • Quantum entanglement approach to strongly-correlated systems
    • Minimum entropy states for quasi-particle statistics and braiding
    • Momentum polarization for chiral topological order
    • Entanglement characterization of emergent Fermi surfaces and non-Fermi liquids
 
  • Quantum oscillations and magneto-transport signatures in realistic materials
    • Quantum oscillations and magneto-transport in Weyl and Dirac semimetals
    • Quantum oscillations in the presence of charge density waves, bilayer structures, and nematicity
    • Effective theory of quasi-periodic systems and cyclotron electrons

 

  • Other computational methods
    • Variational Monte Carlo and Quantum Monte Carlo methods
    • Recursive Green's function method
    • Exact diagonalization method

https://sites.google.com/site/frankzhangyi/home

Scheduled Talks

Machine learning approaches for condensed matter systems   on   Tue, 06/12/2018 - 2:30pm

Yi "Frank" Zhang will first give an overview of the artificial-neural-network-based machine learning, and how its features allow us to approach condensed matter systems from a distinct perspective. In a close comparison with image recognization, Frank will discuss how the artificial neural networks can be trained to identify phases based upon data from experiments or simulations on microscopic models, such as Monte Carlo samples from Ising models and STM images. 

 

Then he will present methods to cater machine learning towards quantum many-body systems pummeled by the 'big data' issue, and illustrate the importance of selection and extraction of information such as Quantum Loop Topography and quantum entanglement. I'll also give a brief introduction on the interpretability of the artificial neural networks. 

Machine learning approaches for condensed matter systems   on   Thu, 06/14/2018 - 11:00am

Yi "Frank" Zhang will first give an overview of the artificial-neural-network-based machine learning, and how its features allow us to approach condensed matter systems from a distinct perspective. In a close comparison with image recognization, Frank will discuss how the artificial neural networks can be trained to identify phases based upon data from experiments or simulations on microscopic models, such as Monte Carlo samples from Ising models and STM images. 

 

Then he will present methods to cater machine learning towards quantum many-body systems pummeled by the 'big data' issue, and illustrate the importance of selection and extraction of information such as Quantum Loop Topography and quantum entanglement. I'll also give a brief introduction on the interpretability of the artificial neural networks.