A comprehensive workflow integrating molecular design rules, DFT calculations, and machine learning for the prediction and optimization of photophysical properties in molecular systems.
Application of advanced machine learning algorithms to predict aggregation-induced emission (AIE) characteristics, achieving high accuracy in identifying promising molecular candidates.
Visual representation of molecular assembly and interaction using a creative, Lego-inspired 3D modeling approach to highlight the modularity and precision in chemical design.
We are actively looking for new group members at all levels.
SCUT undergraduate students interested in joining the group please shoot Shidang an email.
Undergraduate students interested in joining the group for their PhD/Master are encouraged to apply for admission to an SCUT graduate program.
Graduate students admitted to BMSE or other departments at SCUT, as well as those from other universities, who are interested in working with us should contact Shidang.
We are also on the lookout for outstading postdoctoral researchers/research assistants with a background in any combination of the following: Biology, Artificial Intelligence, Medicine, Organic Chemistry, Computational Chemistry, Materials Science, or Physics. Python chops and experience using databases are a big plus.