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Hanning Chen

Research Associate

HPC Performance & Architectures Group


Hanning Chen joined TACC's High Performance Computing (HPC) group in 2022. His research interests include development of ab initio theory, implementation of quantum algorithm, and optimization of large-scale molecular simulation software. His research projects have secured financial support from the National Science Foundation (NSF), U.S. Department of Energy (DOE), U.S. Department of Agriculture (USDA), U.S. Department of Defense (DOE), and Honda Research Institute (HRI). Prior to TACC, he taught chemistry at George Washington University and American University. Hanning is an expert of molecular modeling packages such as CP2K, NWCHEM and GROMACS, and has recently become interested in developing molecular machine learning applications based on TensorFlow, PyTorch and Graphormer.


Areas of Research

Computational and Theoretical Chemistry

Molecular Machine Learning

High Performance Computing

Current Projects

High Performance Computing


Selected Publications

Two-dimensional Electronic-Vibrational Sum Frequency Spectroscopy for Interactions of Electronic and Nuclear Motions at Interface, Proceedings of the National Academy of Sciences, 2021, 118, e2100608118

Symmetry-breaking Enhanced Herzberg-Teller Effect with Brominated Polyacenes, Journal of Physical Chemistry A. 2021, 125, 3589-3599

Functional Mode Singlet Fission Theory, Journal of Physical Chemistry C. 2017, 121, 4130-4138


Education

B.S., Chemistry
University of Science and Technology of China

M.S., Chemistry
University of New Orleans

Ph.D., Chemistry
University of Utah

Organization Membership/Professional Affiliations

Member, American Chemical Society