Methoden vergleichen

	Born-Oppenheimer-Näherung	Dichtefunktionaltheorie
Fachgebiet	Quantencomputing	Quantencomputing
Familie	Machine learning	Machine learning
Entstehungsjahr	1927	1965
Urheber	Max Born and Julius Robert Oppenheimer	Walter Kohn
Typ	Fundamental approximation	Electronic structure method
Wegweisende Quelle	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.
Aliasnamen	BO approximation, clamped nuclei	DFT, Kohn-Sham equations
Verwandt	3	4
Zusammenfassung	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.