Universal Machine Learning Kohn-Sham Hamiltonian for Materials

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外文摘要：While density functional theory(DFT)serves as a prevalent computational approach in electronic structure calculations,its computational demands and scalability limitations persist.Recently,leveraging neural networks to parameterize the Kohn-Sham DFT Hamiltonian has emerged as a promising avenue for accelerating electronic structure computations.Despite advancements,challenges such as the necessity for computing extensive DFT training data to explore each new system and the complexity of establishing accurate machine learning models for multi-elemental materials still exist.Addressing these hurdles,this study introduces a universal electronic Hamiltonian model trained on Hamiltonian matrices obtained from first-principles DFT calculations of nearly all crystal structures on the Materials Project.We demonstrate its generality in predicting electronic structures across the whole periodic table,including complex multi-elemental systems,solid-state electrolytes,Moiré twisted bilayer heterostructure,and metal-organic frameworks.Moreover,we utilize the universal model to conduct high-throughput calculations of electronic structures for crystals in GNoME datasets,identifying 3940 crystals with direct band gaps and 5109 crystals with flat bands.By offering a reliable efficient framework for computing electronic properties,this universal Hamiltonian model lays the groundwork for advancements in diverse fields,such as easily providing a huge data set of electronic structures and also making the materials design across the whole periodic table possible.

作者：

钟阳、于宏宇、杨吉辉、郭星宇、向红军、龚新高

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作者单位：

Key Laboratory of Computational Physical Sciences(Ministry of Education),Institute of Computational Physical Sciences,State Key Laboratory of Surface Physics,and Department of Physics,Fudan University,Shanghai 200433,China

Shanghai Qi Zhi Institute,Shanghai 200030,China

基金：

National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaGuangdong Major Project of the Basic and Applied Basic Research(Future Functional Materials Under Extreme Conditions)

项目编号：

2022YFA14029011182540311991061121881012021B0301030005

出版年：

2024

DOI：

10.1088/0256-307X/41/7/077103

中国物理快报(英文版)

中国科学院物理研究所,中国物理学会

中国物理快报(英文版)

CSTPCDEI

影响因子：0.515

ISSN：0256-307X

年,卷(期)：2024.41(7)