Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Aproximación de Born-Oppenheimer× | Teoría del Funcional de la Densidad× | |
|---|---|---|
| Campo | Computación cuántica | Computación cuántica |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1927 | 1965 |
| Autor original≠ | Max Born and Julius Robert Oppenheimer | Walter Kohn |
| Tipo≠ | Fundamental approximation | Electronic structure method |
| Fuente seminal≠ | Born, M., Oppenheimer, J. R. (1927). Zur Quantentheorie der Moleküle. Annalen der Physik, 84, 457–484. DOI ↗ | Kohn, W., Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133–A1138. DOI ↗ |
| Alias | BO approximation, clamped nuclei | DFT, Kohn-Sham equations |
| Relacionados≠ | 3 | 4 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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