Our faculty members publish their works in prestigious research journals. Take a look at what we have been working on here.
Department Highlights
MSc Applied Physics Admissions 2020/21 [Application deadline: 30 April]
28 November 2019The Department of Physics, City University of Hong Kong, will launch a new taught postgraduate programme, Master of Science in Applied Physics, in 2020/21.
World’s first experimental observation of a Kondo cloud
13 Mar 2020Physicists have been trying to observe the quantum phenomenon Kondo cloud for many decades.
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CityU physics: east meets west on a growth trajectory
13 Mar 2020CityU Physics may be a relative newcomer, but faculty and students are taking advantage of its location and their global connections to build a regional powerhouse for research and education.
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Low-dimensional Systems
Observation of the Kondo screening cloud
Nature
In research published in Nature, an international research group has ended a fifty-year quest by directly observing a quantum phenomenon known as a Kondo screening cloud. This phenomenon, which is important for many physical phenomena such as high-temperature superconductivity, is essentially a cloud that masks magnetic impurities in a material. It was known to exist but its spatial extension had never been experimentally observed, creating controversy over whether such an extension actually existed.
Read more at Nature:
https://www.nature.com/articles/s41586-020-2058-6
The paper has also featured in CityU Research:
https://www.cityu.edu.hk/research/stories/2020/03/12/worlds-first-experimental-observation-kondo-cloud
11 Mar 2020
Soft Matter and Biophysics
Identifying knot types of polymer conformations by machine learning
Physical Review E
We investigate the use of artificial neural networks (NNs) as an alternative tool to current analytical methods for recognizing knots in a given polymer conformation. The motivation is two-fold. First, it is of interest to examine whether NNs are effective at learning the global and sequential properties that uniquely define a knot. Second, knot classification is an important and unsolved problem in mathematical and physical sciences, and NNs may provide new insights into this problem. Motivated by these points, we generate millions of polymer conformations for five knot types, and design various NN models for classification. Our best model achieves a five-class classification accuracy of above 99% on a polymer of 100 monomers. We find that the sequential modeling ability of recurrent NNs is crucial for this result, as it outperforms feed-forward NNs, and successfully generalizes to differently-sized conformations as well. We present our methods and suggest that deep learning may be used in specific applications of knot detection where some error is permissible. Hopefully, with further development, NNs can offer an alternative computational method for knot identification and facilitate knot research in mathematical and physical sciences.
Read more at Physical Review E:
https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.022502
The paper has also featured in Physics magazine:
https://physics.aps.org/synopsis-for/10.1103/PhysRevE.101.022502
Moreover, the paper was highlighted in Nature as well:
https://www.nature.com/articles/d41586-020-00483-w
11 Feb 2020
Education
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