方法对比

	Born-Oppenheimer Approximation	密度泛函理论
领域	量子计算	量子计算
方法族	Machine learning	Machine learning
起源年份	1927	1965
提出者	Max Born and Julius Robert Oppenheimer	Walter Kohn
类型	Fundamental approximation	Electronic structure method
开创性文献	Born, M., Oppenheimer, J. R. (1927). Zur Quantentheorie der Moleküle. Annalen der Physik, 84, 457–484.	Kohn, W., Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133–A1138.
别名	BO approximation, clamped nuclei	DFT, Kohn-Sham equations
相关	3	4
摘要	The Born-Oppenheimer (BO) Approximation is a foundational assumption in molecular quantum mechanics that nuclei can be treated as fixed while solving for electrons, and vice versa. Introduced by Born and Oppenheimer in 1927, this separation reduces the complex many-body electronic-nuclear problem to a sequence of simpler problems, enabling nearly all molecular calculations.	Density Functional Theory (DFT) is a computational method for determining the properties of materials and molecules by modeling the ground state electron density. Developed by Walter Kohn and Lu Jeu Sham in the 1960s, DFT reduces the complexity of quantum chemistry from tracking individual electron coordinates to optimizing the total electron density, enabling efficient simulations of large molecular and condensed-matter systems.