Machine Learning-Assisted Asymmetric Photocatalysis Induced by Circularly Polarized Light
Asymmetric synthesis of single-enantiomer chemicals plays a vital role in producing single-enantiomer drugs in pharmaceutical industry. This project will use CPL as the natural chiral force to trigger the asymmetric synthesis, that is, asymmetric photocatalysis. GLAD will be used to fabricate polyelemental CNPs, which will serve as the asymmetric catalysts. Precursor molecular substrates will be grafted on the polyelemental CNPs, which will be illuminated with CPL for asymmetric catalysis. Chiral HPLC will be operated to analyse an enantiomeric excess (ee) of the enantioselectivity. ML-assisted design of polyelemental CNPs, together with the simulation of the interactions of molecular substrates and CNPs, will be operated to maximize the ee values. According to the simulation and ML-based design, the optimization of the photocatalytic enantioselectivity will be demonstrated under the illumination of CPL, and the mechanisms will be investigated.
Academic background in Chemistry, Material Science and/or Physics. Experimental experience in nanomaterial fabrication and characterization, chemical synthesis and characterization, and/or optics.