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Nature Materials: Discovery of magnetic monopoles? Really?
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On May 12, 2024, an article from Cambridge University was published in Nature Material. “Monopoles had been predicted theoretically, but this is the first time we’ve actually seen a two-dimensional monopole in a naturally occurring magnet,” said co-author Professor Paolo Radaelli, from the University of Oxford, written on the Cambridge news website. The work invokes hot discussion widely. If you know the basics of electromagnetism, you must know there are no magnetic monopoles. So the question remains: Is the work reliable? Or how do we understand the work? The short answer is the definitions of magnetic monopoles vary.
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publications
Applications of Machine Learning in Relativistic Heavy Ion Physics
Published in SCIENTIA SINICA Physica, Mechanica & Astronomica, 2022
Recommended citation: Zhou M, Luo Y, Song H. Applications of machine learning in relativistic heavy ion physics. SCIENTIA SINICA Physica, Mechanica & Astronomica. 2022 Apr 15; 52(5).
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Bayesian Evaluation of Incomplete Fission Yields
Top 2% in the Challenge Cup!
Computational Discovery of Intermolecular Singlet Fission Materials Using Many-Body Perturbation Theory
Published in The Journal of Physical Chemistry C, 2024
Recommended citation: Wang X, Gao S, Luo Y, Liu X, Tom R, Zhao K, Chang V, Marom N. Computational Discovery of Intermolecular Singlet Fission Materials Using Many-Body Perturbation Theory. The Journal of Physical Chemistry C. 2024 May 1;128(19):7841-64.
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Predicting the excited-state properties of crystalline organic semiconductors using GW+BSE and machine learning
Published in Digital Discovery, 2025
Recommended citation: Gao S, Luo Y, Liu X, Marom N. Predicting the excited-state properties of crystalline organic semiconductors using GW+ BSE and machine learning. Digital Discovery. 2025.
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A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals
Published in Scientific Data, 2025
Recommended citation: Gao S, Liu X, Luo Y, Wang X, Zhao K, Chang V, Schatschneider B, Marom N. PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals. Scientific Data. 2025 Apr 23;12(1):679.
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PAH101 dataset
Published in NOMAD, 2025
This is the PAH101 dataset of GW+BSE calculations for 101 molecular crystals of polycyclic aromatic hydrocarbons (PAHs) on NOMAD.
Recommended citation: Gao S, Liu X, Luo Y, Wang X, Zhao K, Chang V, Schatschneider B, Marom N. PAH101: AGW+ BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals. Scientific Data. 2025 Apr 23;12(1):679.
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Fine-Tuning Pretrained Language Models for Poker Strategy Using Reinforcement Learning
Best poster for Generative AI at CMU!
services
Teaching Assistant
Undergraduate course, Carnegie Mellon University, Department of Physics, January 2024 - May 2024
I honorably served as a teaching assistant for the course Physics I for Engineering Students: Electricity and Magnetism in the spring of 2024. This course is designed for second-year undergraduate students in the colledge of engineering.
Admission Committee Member
Admission, Carnegie Mellon University, Department of Physics, December 2024 - April 2025
I honorably served as a member in the admission committee for the Department of Physics at Carnegie Mellon University for Fall 2025 Ph.D. applicants.
talks
BerkeleyGW Workshop
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