1. Liu,Q.-Y.; Shang, C*, Liu, Z.-P.*, “In situ Active Site for CO Activation in Fe-catalyzed Fischer-Tropsch Synthesis from Machine Learning” ,J. Am. Chem. Soc. 143, 11109−11120 (2021).
2. Peng, Y, Shang, C*, Liu, Z.-P.*, “The Dome of Gold Nanolized for Catalysis” ,Chem. Sci. 12, 5664-5671 (2021).
3. Kang, P.-L.; Shang, C.*, Liu, Z.-P.* Recent Implementations in LASP3.0: Global Neural Network Potential with Multiple Elements and Better Long-Range Description. Chin. J. Chem. Phys. 34, 583-590 (2021).
4. Kang, P.-L., Shang, C.*, Liu, Z.-P.* Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration. Acc. Chem. Res. 53, 2119-2129 (2020).
5. (Book) Shang, C. & Liu, Z.-P.* Stochastic Surface Walking Method and Applications to Real Materials in Handbook of Materials Modeling: Applications: Current and Emerging Materials (eds Wanda Andreoni & Sidney Yip) Springer, Cham, (2019).
6. Kang, P.-L., Shang, C.*, Liu, Z.-P.* Glucose to 5-Hydroxymethylfurfural: Origin of Site-Selectivity Resolved by Machine Learning Based Reaction Sampling. J. Am. Chem. Soc. 141, 20525-20536 (2019).
7. Huang, S.-D., Shang, C.*, Kang, P.-L., Zhang, X.-J.; Liu, Z.-P.* LASP: Fast global potential energy surface exploration. WIREs Comput Mol Sci 9, e1415 (2019).
8. Shang, C.*, Huang, S.-D., Liu, Z.-P.* Massively parallelization strategy for material simulation using high-dimensional neural network potential. J. Comput. Chem. 40, 1091-1096 (2019).
9. Shang, C.*, Zhang, X.-J., Liu, Z.-P.* Crystal phase transition of urea: what governs the reaction kinetics in molecular crystal phase transitions. Phys. Chem. Chem. Phys. 19, 32125-32131 (2017).
10. Shang, C.*, Whittleston, C. S.*, Sutherland-Cash, K. H.* , Wales, D. J.* Analysis of the Contrasting Pathogenicities Induced by the D222G Mutation in 1918 and 2009 Pandemic Influenza A Viruses. J. Chem. Theory Comput. 11, 2307-2314 (2015).