Comparer des méthodes

	Théorie de la fonctionnelle de la densité	Monte-Carlo quantique
Domaine	Informatique quantique	Informatique quantique
Famille	Machine learning	Machine learning
Année d'origine	1965	1953
Auteur d'origine	Walter Kohn	Nicholas Metropolis and colleagues
Type	Electronic structure method	Monte Carlo simulation
Source fondatrice	Kohn, W., Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133–A1138.	Metropolis, N., Rosenbluth, A. W., et al. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092.
Alias	DFT, Kohn-Sham equations	QMC, variational Monte Carlo, diffusion Monte Carlo
Apparentées	4	3
Résumé	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.	Quantum Monte Carlo (QMC) is a stochastic computational method for computing ground state properties of quantum many-body systems. Combining classical Monte Carlo sampling with quantum mechanics, QMC approaches are among the most accurate methods available for electronic structure and condensed matter physics, achieving sub-percent accuracy for many systems.