通过网格投影原子指纹的卷积网络学习自洽电子密度

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-10-24 DOI:10.1038/s41524-024-01433-0
Ryong-Gyu Lee, Yong-Hoon Kim
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引用次数: 0

摘要

三维(3D)电子密度分布(ρ)的自洽场(SCF)生成是密度泛函理论(DFT)和相关第一性原理计算的一个基本方面,如何缩短或绕过SCF环路是电子结构理论从实践和基础两个角度提出的一个关键问题。本文提出了一种机器学习策略--DeepSCF,利用三维卷积神经网络(CNN)学习 SCF ρ 与通过中性原子密度求和构建的初始猜测密度(ρ0)之间的映射。首先在三维网格上对ρ0进行编码,然后将输入特征扩展到ρ0以外的原子指纹,从而实现了DeepSCF的高精度和可移植性。我们的目标是预测残余密度(δρ)而不是ρ本身,鉴于δρ是化学键信息的指标,我们采用了具有不同键合特征的小尺寸有机分子数据集。通过对数据集的原子几何结构进行随机旋转和应变,最终提高了 DeepSCF 的保真度。DeepSCF 的有效性通过一个复杂的基于碳纳米管的 DNA 测序仪模型得到了验证。这项研究证明,电子结构的近视性可以通过 CNN 的空间定位得到最佳表现,从而为各种基于机器学习的原子材料模拟的成功提供了启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints

The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution (ρ) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can shorten or bypass the SCF loop represents a critical question in electronic structure theory from both practical and fundamental standpoints. Herein, a machine learning strategy, DeepSCF, is presented in which the map between the SCF ρ and the initial guess density (ρ0) constructed by the summation of neutral atomic densities is learned using 3D convolutional neural networks (CNNs). High accuracy and transferability of DeepSCF are achieved by first encoding ρ0 on a 3D grid and then expanding the input features to include atomic fingerprints beyond ρ0. The prediction of the residual density (δρ) rather than ρ itself is targeted, and given that δρ is indicative of chemical bonding information, a dataset of small-sized organic molecules featuring diverse bonding characters is adopted. The fidelity of DeepSCF is finally enhanced by subjecting the atomic geometries of the dataset to random rotations and strains. The effectiveness of DeepSCF is demonstrated using a complex carbon nanotube-based DNA sequencer model. This work evidences that the nearsightedness in electronic structure can be optimally represented via the spatial locality in CNNs, offering insight into the success of various machine learning-based atomistic materials simulations.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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