Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
Phys Chem Chem Phys. 2022 Aug 31;24(34):20094-20103. doi: 10.1039/d2cp01810a.
Transferable local pseudopotentials (LPPs) are essential for fast quantum simulations of materials. However, various types of LPPs suffer from low transferability, especially since they do not consider the norm-conserving condition. Here we propose a novel approach based on a deep neural network to produce transferable LPPs. We introduced a generalized Kerker method expressed with the deep neural network to represent the norm-conserving pseudo-wavefunctions. Its unique feature is that all necessary conditions of pseudopotentials can be explicitly considered in terms of a loss function. Then, it can be minimized using the back-propagation technique just with single point all-electron atom data. To assess the transferability and accuracy of the neural network-based LPPs (NNLPs), we carried out density functional theory calculations for the s- and p-block elements of the second to the fourth periods. The NNLPs outperformed other types of LPPs in both atomic and bulk calculations for most elements. In particular, they showed good transferability by predicting various properties of bulk systems including binary alloys with higher accuracy than LPPs tailored to bulk data.
可转移局域赝势（LPPs）对于材料的快速量子模拟至关重要。然而，各种类型的 LPPs 存在转移能力低的问题，特别是因为它们没有考虑范数守恒条件。在这里，我们提出了一种基于深度神经网络的新方法，用于生成可转移的 LPPs。我们引入了一种广义 Kerker 方法，并用深度神经网络来表示范数守恒赝波函数。它的独特之处在于，所有赝势的必要条件都可以根据损失函数明确考虑。然后，它可以使用反向传播技术，仅使用单点全电子原子数据进行最小化。为了评估基于神经网络的 LPPs（NNLPs）的转移能力和准确性，我们对第二到第四周期的 s 和 p 区元素进行了密度泛函理论计算。对于大多数元素，NNLPs 在原子和体计算中都优于其他类型的 LPPs。特别是，它们通过预测包括二元合金在内的体系统的各种性质，表现出良好的转移能力，其准确性高于针对体数据定制的 LPPs。
