Shaoqi Zhan

Shaoqi completed his PhD study at the KTH Royal Institute of Technology, Sweden, where his research focused on using computational approaches for studying the dynamics and reactions of electrocatalysts in realistic contexts. After his graduation, he pursued his postdoctoral research at the University of California, Riverside, and the University of Maryland, USA. He expanded training in nanomaterial modelling using first principle calculations and in mechanistic understanding of kinase reactivity utilising constant pH MD and QM methods.

He was awarded a Vetenskapsrådet postdoc grant from the Swedish Research Council to conduct his research at the University of Oxford, where he focused on supermolecular cage using MD and designing novel metallocages for binding using ML. His recent project granted by Formas early-career research grant focused on computational strategy development for nitrogen fixation.

He is equipped with interdisciplinary expertise and a robust collaborative network from strong mobility. His research aims to use artificial intelligence-powered methods, including data science, ML and computational methods, in collaboration with experimental techniques, to drive novel designs for sustainable catalysis.