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首页> 外文期刊>The journal of physical chemistry, A. Molecules, spectroscopy, kinetics, environment, & general theory >Reaction Enthalpies Using the Neural-Network-Based X1 Approach: The Important Choice of Input Descriptors
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Reaction Enthalpies Using the Neural-Network-Based X1 Approach: The Important Choice of Input Descriptors

机译：使用基于神经网络的X1方法的反应焓：输入描述符的重要选择

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摘要

Artificial neural networks represent a simple but efficient way to model and correct known errors existing between commonly used density functional computations and experimental data. The recently proposed X1 approach combines B3LYP energies with a neural-network correction. The latter receives input from a set of physical descriptors, which are primarily based on B3LYP energies. The method shows remarkable improvements for enthalpies of formation and bond energies, for molecules containing first and second row elements, in comparison to B3LYP. Here, reaction enthalpies of organic compounds containing H, C, N, and O are derived using the X1 method, as well as B3LYP, M05-2X, and G3. Despite the seemingly impressive results obtained with X1, our study reveals that underlying problems with B3LYP descriptions of medium and long-range correlation remain. Thus, X1, like B3LYP, breaks down when describing both linear and branched organic molecules. These deficiencies likely arise from the improper or insufficient selection of physical descriptors. To improve the B3LYP energies by means of a neural-network correction, we stress the importance of considering protobranching-dependent descriptors in the input layer of the neural network.
机译：人工神经网络代表了一种简单而有效的方法，可以对常用的密度泛函计算和实验数据之间存在的已知误差进行建模和校正。最近提出的X1方法将B3LYP能量与神经网络校正相结合。后者从一组物理描述符接收输入，这些描述符主要基于B3LYP能量。与B3LYP相比，该方法显示出对于包含第一和第二行元素的分子，在形成焓和键能上的显着改善。在此，使用X1方法以及B3LYP，M05-2X和G3导出包含H，C，N和O的有机化合物的反应焓。尽管使用X1获得了看似令人印象深刻的结果，但我们的研究表明，B3LYP描述中长期关联的潜在问题仍然存在。因此，当描述线性和支链有机分子时，X1和B3LYP一样分解。这些缺陷可能是由于对物理描述符的选择不正确或不充分造成的。为了通过神经网络校正来提高B3LYP能量，我们强调了在神经网络的输入层中考虑依赖原分支的描述符的重要性。

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