A fundamental invariant-neural network representation of quasi-diabatic Hamiltonians for the two lowest states of H3.
A fundamental invariant-neural network representation of quasi-diabatic Hamiltonians for the two lowest states of H3.
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DOI：
10.1039/d0cp05047d
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Zhengxi Yin;B. Braams;Y. Guan;Bina Fu;Dong H. Zhang
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文献类型：
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作者：
Zhengxi Yin;B. Braams;Y. Guan;Bina Fu;Dong H. Zhang
提出了一种基本不变神经网络（FI-NN）方法，用完全核置换和反转（CNPI）群的二维不可约表示拟准表示耦合势能面。由于D3h点群的E对称性，解决了H3在1A‘和2A’电子态的非绝热势能矩阵的特殊对称性。利用这种具有对称性自适应的FI-NN框架构造了一种新的H3的准准表示，该表示精确地再现了H3的从头算能量和导数信息，具有完美的对称性和极小的拟合误差。新FI-NN非绝热PESs的量子动力学结果在产物状态分辨微分截面上产生了精确的振荡模式。这些结果有力地支持了FI-NN方法在复杂对称问题上构造可靠非绝热表示的准确性和有效性。
The fundamental invariant neural network (FI-NN) approach is developed to represent coupled potential energy surfaces in quasidiabatic representations with two-dimensional irreducible representations of the complete nuclear permutation and inversion (CNPI) group. The particular symmetry properties of the diabatic potential energy matrix of H3 for the 1A' and 2A' electronic states were resolved arising from the E symmetry in the D3h point group. This FI-NN framework with symmetry adaption is used to construct a new quasidiabatic representation of H3, which reproduces accurately the ab initio energies and derivative information with perfect symmetry behaviors and extremely small fitting errors. The quantum dynamics results on the new FI-NN diabatic PESs give rise to accurate oscillation patterns in the product state-resolved differential cross sections. These results strongly support the accuracy and efficiency of the FI-NN approach to construct reliable diabatic representations with complicated symmetry problems.