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経路積分モンテカルロ法×密度汎関数理論×
分野量子コンピューティング量子コンピューティング
系統Machine learningMachine learning
提唱年19481965
提唱者Richard FeynmanWalter Kohn
種類Stochastic simulationElectronic structure method
原典Feynman, R. P. (1948). Space-time approach to non-relativistic quantum mechanics. Reviews of Modern Physics, 20, 367–387. DOI ↗Kohn, W., Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133–A1138. DOI ↗
別名PIMC, Feynman path integralDFT, Kohn-Sham equations
関連34
概要Path Integral Monte Carlo (PIMC) is a computational method for calculating thermodynamic and structural properties of quantum systems using Feynman's path integral formulation. Developed rigorously by David Ceperley and colleagues in the 1990s, PIMC treats quantum particles as classical polymers in a higher-dimensional space, enabling efficient Monte Carlo sampling of quantum statistics.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.
ScholarGateデータセット
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  2. 3 出典
  3. PUBLISHED
  1. v1
  2. 3 出典
  3. PUBLISHED

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ScholarGate手法を比較: Path Integral Monte Carlo · Density Functional Theory. 2026-06-18に以下より取得 https://scholargate.app/ja/compare