论文标题
基于多体相关的Hohenberg-Kohn地图的实用方法：学习电子密度
A practical approach to Hohenberg-Kohn maps based on many-body correlations: learning the electronic density
论文作者
论文摘要
对技术相关领域的材料的高吞吐量筛选，例如确定更好的催化剂，电子材料，用于高温应用的陶瓷和药物发现，是一个新兴的研究主题。为了促进这一点，通常使用基于密度函数理论（DFT）计算来计算各种材料的电子结构。但是，DFT计算很昂贵，计算成本量表是系统中存在的电子数量的立方体。因此，希望生成可以减轻这些问题的替代模型。为此，我们提出了两个步骤，以预测每个原子的化学精度（1kcal/mol）使用小数据集的化学精度（1kcal/mol）的总能量，这意味着可以在现场训练此类模型。我们的程序基于Brockherde等人提出的Hohenberg-Kohn地图的思想。 （Nat。Commun，8，872（2017）），涉及两种培训模型：一种，可以直接从原子结构中预测基态电荷密度，$ρ（r）$，另一种是从$ρ（r）$中预测总能量。为了预测$ρ（r）$，我们使用多体相关描述符来准确描述网格点的邻域并预测我们使用这些多体相关描述符振幅的总能量。利用多体描述符的幅度，可以在考虑约束（例如翻译不变性）时唯一识别结构；另外，这种公式与电荷密度网格无关。
High throughput screening of materials for technologically relevant areas, like identification of better catalysts, electronic materials, ceramics for high temperature applications and drug discovery, is an emerging topic of research. To facilitate this, density functional theory based (DFT) calculations are routinely used to calculate the electronic structure of a wide variety of materials. However, DFT calculations are expensive and the computing cost scales as the cube of the number of electrons present in the system. Thus, it is desirable to generate surrogate models that can mitigate these issues. To this end, we present a two step procedure to predict total energies of large three-dimensional systems (with periodic boundary conditions) with chemical accuracy (1kcal/mol) per atom using a small data set, meaning that such models can be trained on-the-fly. Our procedure is based on the idea of the Hohenberg-Kohn map proposed by Brockherde et al. (Nat. Commun, 8, 872 (2017)) and involves two training models: one, to predict the ground state charge density, $ρ(r)$, directly from the atomic structure, and another to predict the total energy from $ρ(r)$. To predict $ρ(r)$, we use many-body correlation descriptors to accurately describe the neighborhood of a grid point and to predict the total energy we use amplitudes of these many-body correlation descriptors. Utilizing the amplitudes of the many-body descriptors allows for uniquely identifying a structure while accounting for constraints, such as translational invariance; additionally, such a formulation is independent of the charge density grid.
