Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika atkarīgā DFT× | Blīvuma funkcionāļu teorija× | |
|---|---|---|
| Nozare | Kvantu skaitļošana | Kvantu skaitļošana |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1984 | 1965 |
| Autors≠ | Erich Runge and Eberhard Gross | Walter Kohn |
| Tips≠ | Excited state method | Electronic structure method |
| Pirmavots≠ | Runge, E., Gross, E. K. (1984). Density-functional theory for time-dependent systems. Physical Review Letters, 52, 997–1000. DOI ↗ | Kohn, W., Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133–A1138. DOI ↗ |
| Citi nosaukumi | TDDFT, TDDFT/DFT | DFT, Kohn-Sham equations |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | Time-Dependent Density Functional Theory (TDDFT) extends DFT to excited states and time-dependent phenomena. Formulated by Runge and Gross in 1984, TDDFT enables calculation of excitation energies, optical spectra, and charge-transfer processes with moderate computational cost, making it invaluable for photochemistry and materials science. | 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. |
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