In this study, an augmented Graph Convolutional Network (GCN) with quantum mechanics (QM) descriptors was reported for its accurate predictions of NMR chemical shifts with respect to experimental values. The prediction errors of ¹³C/¹H NMR chemical shifts can be as small as 2.14/0.11 ppm. There are two crucial characteristics for this modified GCN: in one aspect, such a novel neural network could efficiently extract the overall molecule structure information; in another aspect, it could accurately solve the chemical environment of the target atom. As there exists an imperfect linear regression between the experimental NMR chemical shifts (δ) and the density functional theory (DFT) calculated isotropic shielding constants (σ), the inclusion of QM descriptors within GCN can largely improve its performance. Moreover, few-shot learning also becomes feasible with these descriptors. The success of this novel GCN in chemical shifts predictions also indicates its potential applicability for other computational studies.

中文翻译：

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通过量子力学描述符，增强图卷积神经网络实现对原子性质的准确预测：这种新方法在NMR化学位移预测中的应用

在这项研究中，报道了具有量子力学（QM）描述符的增强图卷积网络（GCN），以精确预测NMR化学位移相对于实验值的变化。预测误差为¹³ C / ¹1 H NMR化学位移可小至2.14 / 0.11 ppm。这种修饰的GCN具有两个关键特征：一方面，这种新颖的神经网络可以有效地提取总体分子结构信息。另一方面，它可以准确地解决目标原子的化学环境。由于在实验NMR化学位移（δ）和密度泛函理论（DFT）计算的各向同性屏蔽常数（σ）之间存在不完美的线性回归，因此在GCN中包含QM描述符可以大大改善其性能。此外，利用这些描述符进行少量学习也变得可行。这种新型GCN在化学位移预测中的成功也表明了其在其他计算研究中的潜在适用性。