ABSTRACT
Thanks to the physicists for establishing the theoretical framework in quantum chemistry, enabling the consideration and solution of nearly all chemical systems and processes as quantum many-body problems. However, practical computations face two major challenges: i). the exponential dimensionality of Hilbert space, and ii). the chemical systems suffer from inherent complexity and disorder which complicates the interactions between different components, making effective coarse-graining difficult. Numerous approaches have been proposed, among which tensor-train (TT) methods draw a lot of attention. The tensor-train transforms exponentially scaled data into tensor products, thereby circumventing the aforementioned exponential problems. Nevertheless, further treatment is required to address issue ii). In this seminar, we will introduce our proposed mapping method for TT and demonstrate its effectiveness in non-adiabatic dynamics simulations and absorption peak calculations.
BIOGRAPHY
Yihe received the B.S. degree in chemistry from Nanjing University, Nanjing, China in 2019, and the Ph. D. degree in theoretical and computational chemistry in 2025 under the supervision of Prof. Haibo Ma. His research interests include development of novel tensor-train-based methods and programs for non-adiabatic processes, ultrafast spectroscopy and other similar fields. He has seven publications in top journals including Nature Chemistry, JACS Au, J. Comput. Theory Chem., J. Chem. Phys.
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