Gorde:
| Egile Nagusiak: | , |
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| Formatua: | Recurso digital |
| Hizkuntza: | |
| Argitaratua: |
Zenodo
2025
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| Sarrera elektronikoa: | https://doi.org/10.5281/zenodo.17743586 |
| Etiketak: |
Etiketa erantsi
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Aurkibidea:
- The quest for chemical accuracy in computational chemistry remains a central challenge, balancing the competing demands of computational efficiency and predictive power. This paper explores the integration of Density Functional Theory (DFT) and post-Hartree-Fock (post-HF) methods, two pillars of modern quantum chemistry, to achieve unprecedented precision in molecular simulations. DFT offers a computationally affordable pathway to describe electron correlation, making it suitable for large systems, but often falls short of chemical accuracy for diverse properties due to approximations in its exchange-correlation functionals. Conversely, post-HF methods, such as coupled cluster theory, provide systematically improvable accuracy, approaching the exact solution of the Schrödinger equation, but their steep computational cost limits their applicability to smaller systems. This work reviews the theoretical underpinnings, strengths, and limitations of both approaches, proposing and evaluating various strategies for their synergistic combination. We discuss composite methods, double-hybrid functionals, and quantum embedding techniques as promising avenues to bridge the gap between efficiency and accuracy. Through a comprehensive analysis, this paper highlights how such integrated methodologies can overcome the individual shortcomings of DFT and post-HF methods, enabling reliable predictions of thermochemical properties, reaction mechanisms, and spectroscopic parameters for a wide range of chemical systems, ultimately pushing the boundaries of computational chemistry towards truly predictive modeling. The discussion encompasses the challenges associated with method development and practical applications, along with future directions for achieving robust and widely applicable precision quantum chemistry tools.