密度泛函理论
从头算
哈密顿量(控制论)
计算机科学
范德瓦尔斯力
维数之咒
人工神经网络
深度学习
统计物理学
人工智能
计算化学
物理
量子力学
数学
数学优化
化学
分子
作者
He Li,Zun Wang,Nianlong Zou,Meng Ye,Runzhang Xu,Xinyi Gong,Wenhui Duan,Yong Xu
标识
DOI:10.1038/s43588-022-00265-6
摘要
The marriage of density functional theory (DFT) and deep-learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent the DFT Hamiltonian (DeepH) of crystalline materials, aiming to bypass the computationally demanding self-consistent field iterations of DFT and substantially improve the efficiency of ab initio electronic-structure calculations. A general framework is proposed to deal with the large dimensionality and gauge (or rotation) covariance of the DFT Hamiltonian matrix by virtue of locality, and this is realized by a message-passing neural network for deep learning. High accuracy, high efficiency and good transferability of the DeepH method are generally demonstrated for various kinds of material system and physical property. The method provides a solution to the accuracy–efficiency dilemma of DFT and opens opportunities to explore large-scale material systems, as evidenced by a promising application in the study of twisted van der Waals materials. A deep neural network method is developed to learn the mapping function from atomic structure to density functional theory (DFT) Hamiltonian, which helps address the accuracy–efficiency dilemma of DFT and is useful for studying large-scale materials.
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