报告时间：4月4日下午2:30-5:00

报告地点：近代物理系210会议室

报告人：清华大学高等研究院 翟荟研究员

报告题目：Topological Trio

报告摘要：Topological physics has been studied for more than three decades, can we still find something new ? In this talk I will present three answers from our recent work:

Non-Equlibirum Dynamics: Previous studies of topological effect are mostly focused on equilibrium or near equilibrium situation, here we will show that the topological invariant can also manifest its physical effect in a quench dynamics far from equilibrium.

Machine Learning: We show that we can train a neural network to accurately predict topological invariant from local input and without human knowledge as a prior. We also analyze the neural network to show that what is captured by the neural network is precisely the same mathematical formula for topological invariant.

Interaction Effect: We utilize the recently proposed Sachdev-Ye-Kitaev model and construct an exactly solvable model to address the interaction effect in a topological band insulator. An interaction induced topological transition and its critical behaviors are shown explicitly by this model.

Reference:

1. Ce Wang, Pengfei Zhang, Xin Chen, Jinlong Yu and Hui Zhai, Phys. Rev. Lett. 118, 185701 (2017)

2. Pengfei Zhang, Huitao Shen and Hui Zhai, Phys. Rev. Lett. 120, 066401 (2018)

3. Pengfei Zhang and Hui Zhai, arXiv: 1803.01411