পদ্ধতির তুলনা করুন

	বর্ন-অপেনহাইমার আসন্নীকরণ (Born-Oppenheimer Approximation)	ঘনত্ব কার্যকরী তত্ত্ব
ক্ষেত্র	কোয়ান্টাম কম্পিউটিং	কোয়ান্টাম কম্পিউটিং
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1927	1965
প্রবর্তক	Max Born and Julius Robert Oppenheimer	Walter Kohn
ধরন	Fundamental approximation	Electronic structure method
মৌলিক উৎস	Born, M., Oppenheimer, J. R. (1927). Zur Quantentheorie der Moleküle. Annalen der Physik, 84, 457–484.	Kohn, W., Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133–A1138.
অপর নাম	BO approximation, clamped nuclei	DFT, Kohn-Sham equations
সম্পর্কিত	3	4
সারসংক্ষেপ	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.