期刊
JOURNAL OF CHEMICAL PHYSICS
卷 150, 期 21, 页码 -出版社
AMER INST PHYSICS
DOI: 10.1063/1.5099106
关键词
-
资金
- Department of Energy [DE-SC0015997]
In a previous paper, we have demonstrated that artificial neural networks (NNs) can be used to generate quasidiabatic Hamiltonians (H-d) that are capable of representing adiabatic energies, energy gradients, and derivative couplings. In this work, two additional issues are addressed. First, symmetry-adapted functions such as permutation invariant polynomials are introduced to account for complete nuclear permutation inversion symmetry. Second, a partially diagonalized representation is introduced to facilitate a better description of near degeneracy points. The diabatization of 1, 2(1)A states of NH3 is used as an example. The NN fitting results are compared to that of a previous fitting with symmetry adapted polynomials.
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