Chenchen Song

Our research in theoretical chemistry focuses on developing quantum chemistry and excited state dynamics methods from first principles to provide understanding of photochemical processes and their practical applications. To achieve high performance, we combine quantum chemistry methods with latest advances in computer science (e.g. specialized hardware like graphical processing units (GPUs)) as well as mathematical methods(e.g. tensor factorizations). We are interested in applying the new theoretical tools to both photochemistry in life (e.g. bioluminescence, visual processes) , as well as renewable energy applications (e.g. light harvesting complexes).

Education, Awards and Professional Highlights

• Assistant Professor, UC Davis (2021)
• Postdoctoral Fellow, University of California, Berkeley and Lawrence Berkeley National Lab (2018-2021)
• Ph.D. in Chemistry, Stanford University (2018)
• B.S. in Chemistry, Tsinghua University (2012)

Representative Publications

• Song, C.C., New physical insights into the supporting subspace factorization of XMS-CASPT2 and generalization to multiple spin states via spin-free formulation, J. Chem. Phys. 160, 124106 (2024)
• Song, C.C., State averaged CASSCF in AMOEBA polarizable water model for simulating nonadiabatic molecular dynamics with nonequilibrium solvation effects. J. Chem. Phys. 158, 014101 (2023). (Editor's Pick)
• Song, C.C., State-averaged CASSCF with polarizable continuum model for studying photoreactions in solvents: Energies, analytical nuclear gradients, and nonadiabatic couplings. J. Chem. Phys. 156, 104102 (2022).
• Song, C.C.; Neaton, J.B.; Martínez, T.J., Reduced scaling formulation of CASPT2 analytical gradients using the supporting subspace method. J. Chem. Phys., 2021, 154, 014103.
• Song, C.C.; Martínez, T.J., Reduced scaling extended multi-state CASPT2 (XMS-CASPT2) using supporting subspaces and tensor hyper-contraction. J. Chem. Phys. 2020, 152, 234113.
• Song, C.C.; Martínez, T.J., Reduced scaling CASPT2 using supporting subspaces and tensor hyper-contraction. J. Chem. Phys. 2018, 149, 044108.
• Song, C.C.; Martínez, T.J., Analytical Gradients for Tensor Hyper-Contracted MP2 and SOS-MP2 on Graphical Processing Units. J. Chem. Phys. 2017, 147, 161723.
• Song, C.C.; Martínez, T.J., Atomic Orbital-Based SOS-MP2 with Tensor Hypercontraction: II. Local Tensor Hypercontraction. J. Chem. Phys. 2017, 146, 034104.
• Song, C.C.; Martínez, T.J., Atomic Orbital-Based SOS-MP2 with Tensor Hypercontraction: I. GPU-based Tensor Construction and Exploiting Sparsity. J. Chem. Phys. 2016, 144, 174111.
• Song, C.C.; Wang, L-P.; Martínez, T.J., Automated Code Engine for Graphical Processing Units: Application to ECP Integrals and Gradients. J. Chem. Theory Comput. 2016, 12(1), 92-106.
• Song, C.C.; Wang, L-P.; Sachse, T.; Press, J.; Presselt, M.; Martínez, T.J., Efficient Implementation of Effective Core Potential Integrals and Gradients on Graphical Processing Units (GPUs). J. Chem. Phys. 2015, 143, 014114.