Vibrational energies for HFCO using a neural network sum of exponentials potential energy surface.
Vibrational energies for HFCO using a neural network sum of exponentials potential energy surface.
复制标题
使用指数势能面的神经网络总和计算 HFCO 的振动能。
DOI:
10.1063/1.4948440
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发表时间:
2016
期刊:
影响因子:
--
通讯作者:
Alex Brown
中科院分区:
文献类型:
--
作者:
Ekadashi Pradhan;Alex Brown
A six-dimensional potential energy surface (PES) for formyl fluoride (HFCO) is fit in a sum-of-products form using neural network exponential fitting functions. The ab initio data upon which the fit is based were computed at the explicitly correlated coupled cluster with single, double, and perturbative triple excitations [CCSD(T)-F12]/cc-pVTZ-F12 level of theory. The PES fit is accurate (RMSE = 10 cm(-1)) up to 10 000 cm(-1) above the zero point energy and covers most of the experimentally measured IR data. The PES is validated by computing vibrational energies for both HFCO and deuterated formyl fluoride (DFCO) using block improved relaxation with the multi-configuration time dependent Hartree approach. The frequencies of the fundamental modes, and all other vibrational states up to 5000 cm(-1) above the zero-point energy, are more accurate than those obtained from the previous MP2-based PES. The vibrational frequencies obtained on the PES are compared to anharmonic frequencies at the MP2/aug-cc-pVTZ and CCSD(T)/aug-cc-pVTZ levels of theory obtained using second-order vibrational perturbation theory. The new PES will be useful for quantum dynamics simulations for both HFCO and DFCO, e.g., studies of intramolecular vibrational redistribution leading to unimolecular dissociation and its laser control.
影响因子:
1.7
作者:
H. Werner;G. Knizia;F. Manby
通讯作者:
H. Werner;G. Knizia;F. Manby