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首页> 外文期刊>The journal of physical chemistry, A. Molecules, spectroscopy, kinetics, environment, & general theory >A nested molecule-independent neural network approach for high-quality potential fits
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A nested molecule-independent neural network approach for high-quality potential fits

机译：嵌套分子独立神经网络方法可实现高质量的潜在拟合

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摘要

It is shown that neural networks (NNs) are efficient and effective tools for fitting potential energy surfaces. For H2O, a simple NN approach works very well. To fit surfaces for HOOH and H2CO, we develop a nested neural network technique in which we first fit an approximate NN potential and then use another NN to fit the difference of the true potential and the approximate potential. The root-mean-square error (RMSE) of the H2O surface is I cm(-1). For the 6-D HOOH and H2CO surfaces, the nested approach does almost as well attaining a RMSE of 2 cm(-1). The quality of the NN surfaces is verified by calculating vibrational spectra. For all three molecules, most of the low-lying levels are within 1 cm(-1) of the exact results. On the basis of these results, we propose that the nested NN approach be considered a method of choice for both simple potentials, for which it is relatively easy to guess a good fitting function, and complicated (e.g., double well) potentials for which it is much harder to deduce an appropriate fitting function. The number of fitting parameters is only moderately larger for the 6-D than for the 3-D potentials, and for all three molecules, decreasing the desired RMSE increases only slightly the number of required fitting parameters (nodes). NN methods, and in particular the nested approach we propose, should be good universal potential fitting tools.
机译：结果表明，神经网络（NNs）是拟合势能面的有效工具。对于H2O，简单的NN方法非常有效。为了拟合HOOH和H2CO的表面，我们开发了一种嵌套神经网络技术，在该技术中，我们首先拟合一个近似NN势，然后使用另一个NN拟合真实势和近似势的差。 H2O表面的均方根误差（RMSE）为I cm（-1）。对于6-D HOOH和H2CO表面，嵌套方法几乎可以达到2 cm（-1）的RMSE。通过计算振动光谱来验证NN表面的质量。对于所有三个分子，大多数低洼水平都在精确结果的1 cm（-1）内。基于这些结果，我们建议将嵌套神经网络方法考虑为简单势和相对复杂的势（例如，双势阱）的简单选择，对于简单势来说，这很容易猜测出良好的拟合函数;很难推断出合适的拟合函数。对于6-D，拟合参数的数量仅比3-D电势适度地大，并且对于所有三个分子，降低所需的RMSE只会稍微增加所需的拟合参数（节点）的数量。 NN方法，尤其是我们提出的嵌套方法，应该是很好的通用潜在拟合工具。

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