Acceleration Computational Design: From Building-Blocks to Novel Catalysts and Nanomaterials

Nature provides stunning examples that show how the self-assembly of relatively simple building blocks leads to the formation of sophisticated functions.

The generation of these systems has inspired scientists to design biomimetic alternatives using non-covalent interactions. Among them, self-assembled metallocages are arising as promising alternatives. To date, metallocages have shown versatile applications, including sensing and catalysis. However, their designs remain a time-consuming and trial-and-error endeavour.

Therefore, the primary purpose of this project is to develop streamlined computational strategies to rationalize the features driving binding and catalysis in known metallocages and use this knowledge to discover novel functional metallocages. Efforts will be focused on the design of metallocages for sensing and catalysis.

The predicted promising systems will be tested via interdisciplinary collaborations.To successfully deliver the project, the research program is well organized with three work packages, including methods and models development (Timeframe 40%), sensing (Timeframe 40%), and catalysis (Timeframe 20%), each providing a complementary core component.

The implementation methods are cutting-edge methodologies, including DFT, DLPNO-CC, AIMD and semi-empirical methods, ML models, and experimental skills.

The success of the research will greatly expand the state-of-the-art in computational modelling, host-guest self-assembly, catalysis and nanomaterials.