The quantum mechanical and molecular mechanical (QM/MM) method combined with molecular dynamics (MD) simulation by now still faces many challenges, such as the slow sampling for the MM subsystem and the demanding cost on the QM subsystem. To address the first issue, we developed a resolution-adapted method that spans three levels of resolution as quantum mechanical, all-atomic and coarse-grained models. To address the second issue, we developed a neural network method for ab initio QM/MM MD simulation based on semiempirical QM/MM MD sampling. The free energy changes on redox processes and aqueous reactions can be reproduced more accurately and efficiently.
Dr. Lin Shen received his B.S degree in 2007 and Ph.D. degree in 2012, both from Beijing Normal University. He continued his research as a postdoctoral associate at the University of Hong Kong and Duke University from 2012 to 2018. He developed several methods in the field of multiscale QM/MM and AA+CG modelling, machine learning based molecular dynamics and enhanced sampling techniques. Now he is an associate professor in the College of Chemistry at Beijing Normal University. His current research focuses on methodology development for multiscale nonadiabatic simulation in computational photochemistry and photobiology.
Bi-Weekly Seminar Series by the NYU-ECNU Center for Computational Chemistry at NYU Shanghai