PHYS3151: Machine learning in Physics

HKU PHYS3151: Machine Learning in Physics
Purpose of the Course:
Machine learning is a technique that enables computers to learn without being explicitly "programmed''. It is an essential part of big data science and has been widely used in different fields of physics. This course introduces the basics of machine learning, from key concepts to practical algorithms, with a focus on real-world applications in physics. It is an elective course for the computational physics theme. This is also an essential course for those who plan to apply machine learning techniques in their postgraduate studies such as condensed matter physics and astrophysics or in their future work.
Lecturer: Zi Yang Meng
Tutor: Tim Lok Chau 

The different ways of ordering electrons in two dimensional materials

Researchers from the University of Hong Kong, including Drs. Hongyu Lu, Bin-bin Chen and Prof. Zi Yang Meng, teamed up with Profs. Kai Sun from the University of Michigan and Han-Qing Wu from Sun Yat-sen University, discovered different ways of ordering electrons in 2D quantum materials. In particular, the research team focused on the interplay between smectic order and topological order. The exotic states and thermal phase transitions exhibiting such features are expected to be accessed in future experiments. Continue reading

Launch of Official Website for the Meeting on Fractional Chern Insulators

We are pleased to announce the launch of the official website for the meeting "Fractional Chern Insulators: Theory, Numerics and Experiment."

Access the site here: [Fractional Chern Insulators: Theory, Numerics and Experiment].

Join us from September 4 to 7, 2025, at the University of Hong Kong for a comprehensive exploration of advancements in this field. For more details on registration and key topics, please visit the website. We look forward to your participation!

Unraveling the Mystery of Deconfined Quantum Critical Points

A recent study, led by Professor Zi Yang MENG and co-authored by his PhD student Menghan SONG of HKU Department of Physics, in collaboration with researchers from the Chinese University of Hong Kong, Yale University, University of California, Santa Barbara, Ruhr-University Bochum and TU Dresden, has unravelled some of the secrets concealed within the deconfined quantum critical points. Their findings, recently published in the prestigious journal Science Advances, push the boundaries of modern physics and offer a fresh perspective on how quantum matter operates at these enigmatic junctures.

 

Congratulations, Dr. Lu

On April 16, 2025, Mr. Hongyu Lu marked a pivotal achievement by successfully defending his Ph.D. thesis, entitled "Numerical studies of fractional quantum anomalous Hall states: Emergent forms, collective excitations, and phase transitions". His clear and compelling presentation of his research findings from his time at HKU, along with his confident responses to impromptu questions, earned high praise from the Examining Committee, which included Prof. Xi Dai from HKUST, and Profs. Wang Yao and Dong-Keun Ki from HKU. Congratulations to Dr. Hongyu Lu! We wish him continued success and look forward to his contributions as an emerging leader in theoretical condensed matter physics. Continue reading

从《马说》到《Super-moire 说》

世有 moiré，然后有 super-moiré。Moiré 者常有，而 super-moiré 不常有。
Moiré 之 super 者，其原胞尺度可达百纳米，向之作者不知其能 super 而制也。
是 super-moiré 也，其成于常规 moiré 之量子干涉效应，波粒二象性之力良有以也。
以百纳米之原胞，所含磁通之量亦增百倍，Brown-Zak 振荡正比于磁场之现象，昭然于世也。
噫！Brown-Zak，侯世达诸前辈得见今日之数据，其必曰“微斯人，吾谁与归”矣。

Entanglement Microscopy: How Matter Entangles in Quantum Many-Body Systems

A collaboration between Mr. Ting-Tung Wang, Mr. Menghan Song, and Professor Zi Yang Meng from the Department of Physics at The University of Hong Kong (HKU) together with their collaborators Liuke Lyu and William WITCZAK-KREMPA from the University of Montreal has recently developed a novel algorithm in quantum physics known as ‘entanglement microscopy’ that enables visualization and mapping of this extraordinary phenomenon at a microscopic scale. Their study was published in a recent issue of Nature Communications. Using the algorithm, one can uncover the hidden structures of quantum matter, revealing insights that could transform technology and deepen the understanding of the universe. Continue reading