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Updated: Aug 5, 2026

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
Published on: May 27, 2020
Hybrid analytical-neural network modeling of long-range interactions for molecular collisions
Xiangteng Duan1,2, Yingyue Hong1,2, Yuegu Fang1,2
1State Key Laboratory of Chemical Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian 116023, People's Republic of China. jyhuang@dlut.edu.cn.
None:
Long-range interactions play a key role in (ultra)cold molecular collisions, yet describing them accurately remains challenging. In this work, we introduce a simple and general framework for the construction of a long-range potential energy surface that combines an analytical model with neural network (NN) representations. The physically correct radial asymptotic behavior is enforced analytically using a perturbative multipole model, while NNs are employed to capture the dependence of the multipole coupling coefficients on the internal molecular structure and relative orientation. Applications to the H3, Li3, and KRb-KRb systems demonstrate small fitting errors and physically reasonable extrapolation behavior. Quantum molecular dynamics calculations further show that the inclusion of long-range interactions significantly affects the reaction dynamics of the D + H2 and Li + Li2 reactions.
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