Universal Machine Learning Kohn–Sham Hamiltonian for Materials  

作　　者：钟阳 于宏宇 杨吉辉 郭星宇 向红军 龚新高 Yang Zhong;Hongyu Yu;Jihui Yang;Xingyu Guo;Hongjun Xiang;Xingao Gong(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)

机构地区：[1]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 [2]Shanghai Qi Zhi Institute,Shanghai 200030,China

出　　处：《Chinese Physics Letters》2024年第7期95-110,共16页中国物理快报（英文版）

基　　金：supported the National Key R&D Program of China (Grant No.2022YFA1402901);the National Natural Science Foundation of China (Grant Nos.11825403,11991061,and 12188101);the Guangdong Major Project of the Basic and Applied Basic Research (Future Functional Materials Under Extreme Conditions) (Grant No.2021B0301030005)。

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,Moir´e 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.

关 键 词：HAMILTONIAN utilize TWISTED 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] O469[自动化与计算机技术—控制科学与工程]