arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2104.03786cond-mat.mtrl-scicond-mat.dis-nncond-mat.mes-hallphysics.comp-phquant-ph
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深度学习密度泛函理论哈密顿量用于高效从头计算电子结构

Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation

He Li, Zun Wang, Nianlong Zou, Meng Ye, Runzhang Xu, Xiaoxun Gong, Wenhui Duan, Yong Xu

摘要:

密度泛函理论(DFT)和深度学习方法的结合有可能彻底改变现代计算材料科学。在这里,我们开发了一种深度神经网络方法来表示晶体材料的DFT哈密顿量(DeepH),旨在绕过DFT的计算要求高的自洽场迭代,并大大提高从头计算电子结构计算的效率。提出了一种利用局部性处理DFT哈密顿矩阵的高维性和规范(或旋转)协方差的通用框架,并通过用于深度学习的消息传递神经网络实现。DeepH方法的高精度、高效率和良好的可移植性被普遍证明适用于各种材料体系和物理性质。该方法提供了一个解决DFT的准确性和效率的困境,并开辟了探索大规模材料系统的机会,证明了一个有前途的应用研究扭曲的范德华材料。

英文摘要:

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 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 DFT Hamiltonian matrix by virtue of locality and is realized by the 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 systems and physical properties. 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 to study twisted van der Waals materials.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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