Email: wanghaosd_ATA_sdu._edu._cn (Please remove _ and change ATA to @)
Education/Work Experience
2018.10 - 2021.08 Postdoc Fellow, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, USA
2013.08 – 2018.09 Ph.D. in Theoretical Chemistry, Duke University, USA
2009.09 – 2013.07 B.S. in Physics, Shandong University, China
Research Interests
Molecular Dynamics (MD) simulation is the main theoretical tool for studying biological macromolecular systems, such as proteins, nucleic acids and membranes. Its accuracy primarily depends on the force field and sampling method. Dr. Wang has been engaged in developing new methods to improve the accuracy and efficiency of MD in simulating practical biophysical problems, including polarizable force fields for small molecules, protein and water force fields based on quantum mechanical fragmentation method and neural network, and novel enhanced sampling method. Our current research interests include:
1. developing coarse-graining force fields for proteins.
2. developing rare event simulation algorithms to extend the time scale of simulations.
3. applying multi-scale simulation technique to study various cell activities (e.g., enzymatic catalysis, protein-ligand binding) at the molecular level.
Selected Publications
1. H. Wang and R. Elber, Milestoning with wind: Exploring the impact of a biasing potential in exact calculation of kinetics, J. Chem. Phys. 152, 224105 (2020).
2. H. Wang, N. Huang, T. Dangerfield, K. A. Johnson, J. Gao, and R. Elber, Exploring the reaction mechanism of HIV reverse transcriptase with a nucleotide substrate, J. Phys. Chem. B 124, 21, 4270-4283 (2020).
3. H. Wang and W. Yang, Toward building protein force fields by residue-based systematic molecular fragmentation and neural network, J. Chem. Theory Comput. 15, 2, 1409-1417 (2019).
4. H. Wang and W. Yang, Force field for water based on neural network, J. Phys. Chem. Lett. 9, 12, 3232-3240 (2018).
5. H. Wang and W. Yang, Determining polarizable force fields with electrostatic potentials from quantum mechanical linear response theory, J. Chem. Phys. 144, 224107 (2016).
