Quantum mechanics methods
How it works
Quantum mechanical simulations are based on the laws of quantum mechanics rather than classical physics. Quantum mechanics states that the energy and other related properties of a molecule can be obtained by solving the Schrödinger equation:
Eq. 1 H = E
For any but the smallest symmetric systems, however, exact solutions to the Schrödinger equation cannot be obtained in practice. Quantum mechanical methods are characterized by their various mathematical approximations to its solution. Several major classes of quantum mechanical methods exist:
Types of quantum methods
Semi-empirical methods--For example, AM1, CNDO, INDO, MINDO/3, MNDO, and PM3, which are implemented in programs such as Gaussian, MOPAC, and Zindo. These methods use parameters derived from experimental data to simplify the computation. They solve an approximate form of the Schrödinger equation that depends on having appropriate parameters available for the type of chemical system in question.
Ab initio methods--Unlike molecular mechanics or semi-empirical quantum mechanics methods, ab initio methods use no experimental parameters in their computations. Instead, these computations are based solely on the laws of quantum mechanics and on the values of the speed of light, the masses and charges of electrons and nuclei, and Planck's constant.
DFT (density functional theory) methods--Similar to other quantum chemistry methods (semiempirical or ab initio), DFT aims at predicting molecular (or solid) geometrical structures and electronic properties, Unlike the other methods, DFT assumes that the electron density, not the wavefunction, is the fundamental quantity that determines properties of molecular or solid systems.
In practical applications, the electron density is calculated from orbitals that are generated by solving the one-electron Schrödinger-type equations, the Kohn-Sham equations.
The programs accessible through the Quantum 1 and Quantum 2 modules in Cerius2 offer all the above methods.
Advantages and disadvantages
Semi-empirical and ab initio methods differ in the trade-off made between accuracy and computational cost. Semi-empirical calculations are relatively inexpensive and provide reasonable qualitative descriptions of molecular systems and fairly accurate quantitative predictions of energies and structures for systems where good parameter sets exist.
In contrast, ab initio computations provide high-quality quantitative predictions for a broad range of systems. They are not limited to any specific class of system. Early ab initio programs were very limited in the size of system they could handle; however, this is not true for modern ab initio programs. On a typical workstation, the energies and related properties for systems containing a dozen heavy atoms can now be computed in just a few minutes. Jobs of up to a few hundred atoms can be handled, and model structures having as many as one hundred atoms can also be predicted on typical workstations. Larger systems can be handled on supercomputers.
Ab initio methods can also handle any type of atom, including metals, and can be used to investigate models in their excited states and in solution.
The accuracy of the DFT method depends mainly on the functional used (local,nonlocal,hybrid) and the quality of the computational parameters such as basis sets and numerical integration grid. The local (LSD: local spin density) and nonlocal (NLSD) approaches can be very efficiently implemented, yielding algorithms that scale better with the size of the system than do ab initio methods. DFT programs are slower than semiempirical programs; however, they can be used for various types of molecular and solid-state systems including organic, organometallic, and metallic systems. The latest DFT approaches, NLSD or hybrid (HF-DFT) methods, can be used to study chemical reactions with consistently satisfactory accuracy.
In other words, I don't know.
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