Research
Currently, our research mainly covers the following four aspects.
1. The development of multi-scale simulation methods
Efficient and accurate calculation of scientific problems with multi-scale characteristics, such as the electronic structure of molecules, complex chemical systems, and “meter-scale” macroscopic phenomena, is a significant challenge for current research. To solve the problems mentioned above, we have developed a series of theoretical methods in recent years, including the enhanced conformational sampling methods DA2/teDA2, the linear scale quantum mechanics method GEBF for the precise calculation of spectra (in collaboration with Prof. Shuhua Li), the polarizable force field method (in collaboration with Prof. Jing Ma), and the disease transmission model SID (in collaboration with Prof. Wei Wang), etc.
Selected publications:
J. Chem. Theory Comput. 2017, 13, 5231. (cover)
J. Phys. Chem. B 2017, 121, 10064.
Adv. Theory Simul. 2019, 2, 1800171.
J. Chem. Theory Comput. 2020, 16, 4631.
J. Chem. Theory Comput. 2020, 16, 2995.（supplementary cover）
Acc. Chem. Res. 2021, 54, 169. (review)
2. The study of structure, properties, and functions of biomolecules
Proteins and nucleic acids are the most important macromolecules for the continuity of life. They carry the genetic blueprint of a cell and carry instructions for the functioning of the cell. The study of their structures and functions, interactions and dynamic changes will help reveal the essence of life phenomena. We carry out in-depth and systematic research on several important protein (especially membrane protein) systems.
Selected publications:
Science 2010, 330, 509.
Nat. Commun. 2011, 2, 354.
Proc. Natl. Acad. Sci. USA 2013, 110, 17332.
Chem. Sci. 2013, 4, 2776.
J. Phys. Chem. Lett. 2013, 4, 3067.
Sci. Rep. 2017, 7, 17749.
iScience 2019, 16, 356.
Protein & Cell 2019, 10, 533.
Biopolymers 2020, 111, e23392.
J. Phys. Chem. B 2021, 125, 5338.
3. Calculation-driven design and application of functional materials
Guided by multi-scale simulation and combined with experimental synthesis and characterization methods, we develop new concepts, strategies, techniques, and reactions related to functional materials (especially catalysts). We aim to elucidate the underlying structural basis, evolutionary principles, and responses governing the exceptional atomic-level properties of materials, and to gain insight into the intrinsic connection between material structure and function.
Selected publications:
J. Phys. Chem. B 2017, 121, 10064.
Anal. Chem. 2019, 91, 6829.
J. Am. Chem. Soc. 2019, 141, 223.
Energy Storage Mater. 2021, 41, 650.
Angew. Chem. Int. Ed. 2021, 60, 17164.
Nature Commun. 2021, 12, 5223.
4. Application of machine learning in complex systems
Machine learning (ML) methods play an increasingly important role in chemistry, biology, materials, and other fields. Studies with ML could help disclose the scientific laws of physical and chemical processes with clear rules but complex evolution through “learning” on a large amount of data. We are applying ML to different research topics, such as molecular dynamics simulations with quantum mechanics accuracy, the retrosynthetic route planning, the collective behavior of the self-propelling system, and the link between posture and brain function.
Selected publications:
Phys. Rev. E 2023, 107, 024411.
J. Chem. Theory Comput. 2023, 19, 4243.