Towards the construction of an accurate kinetic energy density functional and its functional derivative through physics-informed neural networks

IF 1.1 Q3 PHYSICS, MULTIDISCIPLINARY
L. Rincón, L. Seijas, R. Almeida, F. Javier Torres
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引用次数: 1

Abstract

One of the primary obstacles in the development of orbital–free density functional theory is the lack of an accurate functional for the Kohn–Sham non-interacting kinetic energy, which, in addition to its accuracy, must also render a good approximation for its functional derivative. To address this critical issue, we propose the construction of a kinetic energy density functional throught physical- informed neural network, where the neural network’s loss function is designed to simultaneously reproduce the atom’s shell structures, and also, an analytically calculated functional derivative. As a proof-of-concept, we have tested the accuracy of the kinetic energy potential by optimizing electron densities for atoms from Li to Xe.
通过物理信息神经网络构建精确的动能密度泛函及其泛函导数
轨道-自由密度泛函理论发展的主要障碍之一是缺乏Kohn–Sham非相互作用动能的精确泛函,除了其准确性外,还必须为其泛函导数提供良好的近似。为了解决这个关键问题,我们建议通过物理知情神经网络构建动能密度泛函,其中神经网络的损失函数被设计为同时再现原子的壳层结构,以及分析计算的泛函导数。作为概念的证明,我们通过优化从Li到Xe原子的电子密度来测试动能势的准确性。
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来源期刊
Journal of Physics Communications
Journal of Physics Communications PHYSICS, MULTIDISCIPLINARY-
CiteScore
2.60
自引率
0.00%
发文量
114
审稿时长
10 weeks
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