Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Теория функционала плотности× | Квантовый Монте-Карло× | |
|---|---|---|
| Область | Квантовые вычисления | Квантовые вычисления |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1965 | 1953 |
| Автор метода≠ | Walter Kohn | Nicholas Metropolis and colleagues |
| Тип≠ | Electronic structure method | Monte Carlo simulation |
| Основополагающий источник≠ | Kohn, W., Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133–A1138. DOI ↗ | Metropolis, N., Rosenbluth, A. W., et al. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092. DOI ↗ |
| Другие названия≠ | DFT, Kohn-Sham equations | QMC, variational Monte Carlo, diffusion Monte Carlo |
| Связанные≠ | 4 | 3 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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